09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware, software, and peripheral devices for use with mobile computer systems and smartphones, all for medical use in monitoring and analyzing patient physiological activity and in transmitting physiological health data to patient's healthcare providers none of the foregoing specifically directed to cardiology, critical care, or the physical treatment of structural heart disease Software as a Service (SaaS) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring and analyzing patient physiological activity and in transmitting and storing physiological health data none of the foregoing specifically directed to cardiology, critical care, or the physical treatment of structural heart disease
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Downloadable and recorded computer software for use with mobile computer systems, smartphones, and peripheral devices, all for medical use in monitoring, recording, and analyzing personal physiological activity, analyzing ECG readings, and in transmitting physiological health data Software as a Service (SaaS) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring, recording, and analyzing personal physiological activity, analyzing ECG readings, and in transmitting physiological health data; providing online non-downloadable software for medical use in monitoring, recording, and analyzing personal physiological activity, analyzing ECG readings, and in transmitting physiological health data Providing health information in the nature of EKG and heart rate readings and biometric data; providing health information in the nature of EKG and heart rate readings and biometric data by means of online software applications
3.
METHODS AND SYSTEMS FOR ARRHYTHMIA TRACKING AND SCORING
A smart watch to detect a presence of an arrhythmia of a user is disclosed. The smart watch comprising: a processing device and a photoplethysmography (“PPG”) sensor operatively coupled to the processing device, an ECG sensor comprising two or more ECG electrodes and operatively coupled to the processing device, a display operatively coupled to the processing device, and a memory, operatively coupled to the processing device. The memory may have instructions stored thereon that, when executed by the processing device, cause the processing device to: receive PPG data from the PPG sensor, receive ECG data from the ECG sensor, detect, based on the PPG data and the ECG data simultaneously, the presence of an arrhythmia, and provide one or more recommendations to the user based on the PPG data and the ECG data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
Embodiments of the present disclosure provide a monitoring device that combines blood pressure, SpO2, and ECG monitoring functionality into a single handheld device and can measure these parameters simultaneously. The monitoring device may comprise a first sensor comprising an electrode to measure electrical signals corresponding to cardiac activity of a user's heart and a second sensor comprising an electrode as well as an optical sensor to perform a PPG and measure an amount of light absorbed by the blood of the user concurrently with measurement of the electrical signals. The optical sensor may generate a blood pressure signal and an oxygen saturation signal based at least in part on the amount of light absorbed by the blood of the user. The optical sensor may include a neural network trained to estimate blood pressure based on PPG measurements and demographic information of the user, as discussed in further detail herein.
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
Embodiments of the present disclosure provide a mobile electrocardiogram (ECG) sensor comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The ECG sensor further comprises a processing device, operatively coupled to the electrode assembly, the processing device to provide the sensed heart-related signals to a machine learning module trained to predict a twelve-lead QT interval (QTc) value from the mobile ECG sensor comprising less than twelve leads. The ECG sensor also comprises a housing containing the electrode assembly and the processing device.
A digital stethoscope includes a stethoscope housing defining a housing edge. The digital stethoscope also includes a surface region secured to the stethoscope housing at the housing edge, and a number of microphones. The digital stethoscope also includes a processing device disposed within the stethoscope housing and in communication with the microphones. The processing device receives the digital audio data from the microphones.
G10L 25/66 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
H04R 1/04 - Structural association of microphone with electric circuitry therefor
H04R 1/40 - Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by combining a number of identical transducers
Disclosed are devices and methods for determining and memorializing chest electrode placement locations on a user. The device utilizes two datum reference points in the patient's anatomy, the Jugular notch and the Xiphoid process, which are easily found and repeatable and which serve to ensure the device is in the correct position to memorialize chest electrode placement locations. The device may comprise a first plate and a second plate operatively coupled to the first plate so that the second plate slides along a vertical axis of the first plate and the first plate slides along a vertical access of the second plate. The device may further comprise a template grid having a plurality of cells printed thereon. The template grid may have a coordinate system for identifying each of the plurality of cells. The template grid may be modified to indicate a set of chest electrode placement locations on the user.
Disclosed are devices and methods for determining and memorializing chest electrode placement locations on a user. The device utilizes two datum reference points in the patient's anatomy, the Jugular notch and the Xiphoid process, which are easily found and repeatable and which serve to ensure the device is in the correct position to memorialize chest electrode placement locations. The device may comprise a first plate and a second plate operatively coupled to the first plate so that the second plate slides along a vertical axis of the first plate and the first plate slides along a vertical access of the second plate. The device may further comprise a template grid having a plurality of cells printed thereon. The template grid may have a coordinate system for identifying each of the plurality of cells. The template grid may be modified to indicate a set of chest electrode placement locations on the user.
Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable computer software and mobile software applications for monitoring, transmitting, and analyzing personal ECG readings, physiological activity, and individual health trends; Downloadable computer software and mobile software applications featuring artificial intelligence for tracking personal ECG readings and individual health trends over time
11.
AUSCULTATION DEVICE FOR DETERMINING AN OPTIMAL LOCATION FOR CARDIORESPIRATORY AUSCULTATION
Embodiments of the present disclosure relate to determining an optimal location on the body of a person where heart sounds may be optimally heard. The optimal location may be determined at a time prior to the attempted auscultation and ECG data corresponding to the optimal location may be stored in a memory of an auscultation device. Subsequently, when a e.g., physician wishes to listen to the heart sounds of the person, the physician may place the auscultation device at a first location on the patient. The auscultation device may periodically perform an ECG at a current location and use the ECG data at the current location and the ECG data at the optimal location to determine and provide guidance to the physician regarding a direction in which the auscultation device should be moved in order to reach the optimal location.
Disclosed systems and method receive electrocardiogram (EKG) data of a subject. The EKG data comprises a record of at least a full beat of the subject. The EKG data is input into a machine learning model to generate an output. The output can include a segmentation of the full beat or a measurement of a QT interval of the subject.
G16H 30/20 - ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Described herein are methods, systems, and software for monitoring blood pressure. In some embodiments, a using a mobile device is used. The blood pressure monitoring system utilizes heart rate measurements at two separate locations on the body to calculate a differential pulse arrival time which is used to estimate blood pressure. The heart rate measurements can be taken simultaneously, or they can be taken sequentially while simultaneously taking ECG measurement. If taken sequentially, the heart rate measurements are aligned with the ECG to determine the differential. Accurate, inexpensive, and discreet blood pressure monitoring is thus provided.
Disclosed are techniques for training a machine learning model. A training data set comprising a plurality of electrocardiogram (ECG) measurements and corresponding analyte measurements for each of the plurality of ECG measurements is provided. For each ECG measurement of the training data set, an estimated analyte level at a time of the ECG measurement is determined based on the corresponding set of analyte measurements. The estimated analyte level may be determined using statistical estimation techniques. If it is determined that the estimated analyte level at the time of the ECG measurement meets a certainty threshold, the ECG measurement is labeled based on the estimated analyte level at the time of the ECG measurement. A machine learning is trained to predict a level of an analyte based on ECG data, where the training is done using each of the plurality of ECG measurements that are labeled.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
Described herein are systems, devices, and methods for cardiac monitoring. A method may include comparing, using a machine learning (ML) algorithm, an activity level of a user with one or more heart rate parameters of the user to determine whether a discordance is present between the activity level and the one or more heart rate parameters. In response to detecting that a discordance is present, performing an electrocardiogram (ECG) of the user to obtain ECG data of the user. An ML algorithm may be trained to detect an arrythmia using a historical set of training data including the activity level, the one or more heart rate parameters and previously recorded heart rate parameters, and a historical set of ECG data including the ECG data and previously recorded ECG data.
Embodiments of the present disclosure provide systems and methods for performing ECG-based authentication. A set of electrocardiogram (ECG) signals of a user may be received by a computing device from an ECG monitor. The computing device may be associated with a resource to which the computing device controls access. An ECG profile for the user may be determined based on the set of ECG signals. In response to receiving a request to access the resource, an authentication ECG signal may be requested to authenticate the user. The authentication ECG signal may be received from the ECG monitor and compared to the ECG profile of the user to determine whether the authentication ECG signal matches the ECG profile. In response to determining that the authentication ECG signal matches the ECG profile, the user may be authenticated and granted access to the resource.
A digital stethoscope includes a stethoscope housing defining a housing edge. The digital stethoscope also includes a surface region secured to the stethoscope housing at the housing edge, and a number of microphones. The digital stethoscope also includes a processing device disposed within the stethoscope housing and in communication with the microphones. The processing device receives the digital audio data from the microphones.
G10L 25/66 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
H04R 1/04 - Structural association of microphone with electric circuitry therefor
H04R 1/40 - Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by combining a number of identical transducers
Disclosed is an ECG sensor having a packaging label form factor. The ECG sensor may comprise a set of electrodes configured to sense heart-related electrical activity when in contact with skin of a user and produce electrocardiogram (ECG) waveforms representing the sensed heart-related electrical activity. The ECG sensor may further comprise a processor configured to: identify, using a machine learning model, the user based on the ECG waveforms, convert the ECG waveforms to a modulated signal that carries the ECG waveforms representing the sensed heart-related electrical activity, and transmit the modulated signal wirelessly to a computing device using a transmitter. The ECG sensor may also comprise a housing having a first layer and a second layer, wherein the set of electrodes, the processor, and the transmitter are all disposed between the first layer and the second layer of the housing, and wherein the housing has a packaging-label form factor.
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
Embodiments of the present disclosure provide a monitoring device that combines blood pressure, SpO2, and ECG monitoring functionality into a single handheld device and can measure these parameters simultaneously. The monitoring device may comprise a first sensor comprising an electrode to measure electrical signals corresponding to cardiac activity of a user's heart and a second sensor comprising an electrode as well as an optical sensor to perform a PPG and measure an amount of light absorbed by the blood of the user concurrently with measurement of the electrical signals. The optical sensor may generate a blood pressure signal and an oxygen saturation signal based at least in part on the amount of light absorbed by the blood of the user. The optical sensor may include a neural network trained to estimate blood pressure based on PPG measurements and demographic information of the user, as discussed in further detail herein.
A61B 5/0205 - Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
22.
Devices, systems, and methods for physiology monitoring
Described herein are software platforms (or “platforms”), systems and methods for providing effective healthcare to a patient while increasing the efficiency of a healthcare provider. A healthcare provider system may receive physiological data of a user and generate an interface to provide a visualization of the physiological data and identifying information of the user. The system may analyze the physiological data to generate a risk score for the user based at least in part on the physiological data and transmit to a device of the user, a notification comprising the risk score.
G08B 21/04 - Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
23.
Detection of cardiac conditions from reduced lead set ECG
Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
24.
DETECTION OF CARDIAC CONDITIONS FROM REDUCED LEAD SET ECG
Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.
Described herein is an apparatus for monitoring various physiological parameters of a user. The apparatus may include a housing with a small or reduced form factor near the size of a credit or debit card, so as to allow a user to obtain e.g., an electrocardiogram (ECG) measurement from various locations. The apparatus may include an electrode assembly comprising a set of electrodes disposed on the housing to perform an ECG by sensing an electrical signal corresponding to heart activity of a user when in contact with skin of the user and output the electrical signal. The apparatus may detect or generate six or more ECG leads.
Described herein is an apparatus for monitoring various physiological parameters of a user. The apparatus may be in the form of a shopping cart, so as to allow a user to obtain e.g., an ECG measurement while they are using the shopping cart. The apparatus may comprise an electrode assembly including a set of electrodes to perform an electrocardiogram (ECG) by sensing an electrical signal corresponding to heart activity of a user when in contact with skin of the user and output the electrical signal. The apparatus may further include a converter assembly to convert the electrical signal to a modulated signal, and a transmitter to transmit the modulated signal. The apparatus may transmit the modulated signal to a computing device which may receive the modulated signal and determine whether the electrical signal indicates that the user is experiencing a heart condition.
Embodiments of the present disclosure provide systems and methods for performing an ECG search based on a dual ECG and text embedding model. A text machine learning (ML) model may be trained to generate a text embedding based on a received text representation of an ECG diagnosis. The text ML model may be used to train an ECG encoding ML model to generate an ECG embedding based on received ECG leads data. A database may be populated with a plurality of ECG embeddings, each of the plurality of ECG embeddings generated based on ECG leads data of previously diagnosed ECGs. In response to receiving a query ECG, the ECG ML model may generate a query embedding and a similarity score between the query embedding and each of the plurality of ECG embeddings may be determined. The top K results may be sorted based on similarity score, and may be displayed/visualized.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
28.
ECG SEARCH AND INTERPRETATION BASED ON A DUAL ECG AND TEXT EMBEDDING MODEL
Embodiments of the present disclosure provide systems and methods for performing an ECG search based on a dual ECG and text embedding model. A text machine learning (ML) model may be trained to generate a text embedding based on a received text representation of an ECG diagnosis. The text ML model may be used to train an ECG encoding ML model to generate an ECG embedding based on received ECG leads data. A database may be populated with a plurality of ECG embeddings, each of the plurality of ECG embeddings generated based on ECG leads data of previously diagnosed ECGs. In response to receiving a query ECG, the ECG ML model may generate a query embedding and a similarity score between the query embedding and each of the plurality of ECG embeddings may be determined. The top K results may be sorted based on similarity score, and may be displayed/visualized.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
29.
TWELVE-LEAD ELECTROCARDIOGRAM USING ELECTRODES COUPLED BY A SINGLE CABLE
Embodiments of the present disclosure provide an ECG monitoring device that comprises a housing cable comprising a plurality of signal cables positioned therein and a set of electrodes positioned along the housing cable. Each of the set of electrodes may contact a particular location of a user without requiring any muscular activity of the user when the ECG monitoring device is connected to the user. The ECG monitoring device may further comprise a computing device positioned along the housing cable and operatively coupled to each of the four or more electrodes via a respective signal cable of the plurality of signal cables. The computing device may comprise a memory and a processing device operatively coupled to the memory, the processing device to perform, using the four or more electrodes, an electrocardiogram (ECG) of the user.
A61B 5/28 - Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
A61B 5/273 - Connection of cords, cables or leads to electrodes
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/259 - Means for maintaining electrode contact with the body using adhesive means, e.g. adhesive pads or tapes using conductive adhesive means, e.g. gels
A61B 5/254 - Means for maintaining electrode contact with the body by clips
A61B 5/256 - Wearable electrodes, e.g. having straps or bands
30.
MULTI-LEAD ELECTRODE PLACEMENT ON A WEARABLE ELECTROCARDIOGRAM DEVICE
Embodiments of the present disclosure provide a 6-lead electrocardiogram (ECG) monitoring device that may acquire a 6-lead ECG, does not require the use of adhesives for electrodes, provides ECG data for a user on a near instantaneous basis, and provides an easy and non-invasive way for a user to take a 6-lead ECG on the fly. The ECG monitoring device may acquire leads I, II, and III. The ECG monitoring device may derive augmented limb leads to generate a 6-lead ECG and may subsequently generate a full 12-lead ECG. The ECG monitoring device may generate one or more diagnoses based on the 6-lead reading or the 12-lead set.
A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Embodiments of the present disclosure relate to providing GUIs for visualizing ECG data and integrated QT analysis results. A system may comprise an electrocardiogram (ECG) monitoring device to measure an ECG of a user to generate ECG data and transmit the ECG data. The system may further comprise a cloud analysis platform to receive the ECG data, perform a QT analysis based on the ECG data to generate QT analysis results, and transmit the QT analysis results. The system may further comprise a computing device to receive the QT analysis results and provide a graphical user interface (GUI) to visualize the ECG data with the QT analysis results integrated therein.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
33.
Methods and systems for arrhythmia tracking and scoring
A smart watch to detect a presence of an arrhythmia of a user is disclosed. The smart watch comprising: a processing device and a photoplethysmography (“PPG”) sensor operatively coupled to the processing device, an ECG sensor comprising two or more ECG electrodes and operatively coupled to the processing device, a display operatively coupled to the processing device, and a memory, operatively coupled to the processing device. The memory may have instructions stored thereon that, when executed by the processing device, cause the processing device to: receive PPG data from the PPG sensor, receive ECG data from the ECG sensor, detect, based on the PPG data and the ECG data simultaneously, the presence of an arrhythmia, and provide one or more recommendations to the user based on the PPG data and the ECG data.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
Embodiments of the present disclosure provide systems and methods for providing an audio user interface by which a user may be provided audio instructions for measuring physiological parameters using a monitoring device as well as an audio explanation of the results of analysis of the physiological parameters. A cloud service may receive via a smart device a voice command associated with measuring physiological parameters and provide to the smart device, instructions for measuring the physiological parameters. The monitoring device is configured to communicate with the cloud service via the smart device, which may provide the instructions for taking the ECG using the ECG monitoring device to a user as an audio output. The cloud service may receive physiological parameter data measured by the monitoring device and process the data to provide a set of interpretations of the data as well as audio data corresponding to a simplified explanation thereof.
A mobile electrocardiogram (ECG) device is described, comprising an electrode assembly comprising electrodes that sense heart-related signals when in contact with a body of a user, and produce electrical signals representing the sensed heart-related signals. The mobile ECG device further comprises a light-emitting device to facilitate an optimal placement of the electrode on the body of the user. The mobile ECG device further comprises a housing containing the electrode assembly, the converter assembly, the transmitter, and the light-emitting device. A first electrode of the electrode assembly forms a first side of the housing and comprises an opening through which the light-emitting device provides the UV light.
Embodiments of the present disclosure relate to determining an optimal location on the body of a person where heart sounds may be optimally heard. The optimal location may be determined at a time prior to the attempted auscultation and ECG data corresponding to the optimal location may be stored in a memory of an auscultation device. Subsequently, when a e.g., physician wishes to listen to the heart sounds of the person, the physician may place the auscultation device at a first location on the patient. The auscultation device may periodically perform an ECG at a current location and use the ECG data at the current location and the ECG data at the optimal location to determine and provide guidance to the physician regarding a direction in which the auscultation device should be moved in order to reach the optimal location.
A digital stethoscope includes a stethoscope housing defining a housing edge. The digital stethoscope also includes a surface region secured to the stethoscope housing at the housing edge, and a number of microphones. The digital stethoscope also includes a processing device disposed within the stethoscope housing and in communication with the microphones. The processing device receives the digital audio data from the microphones.
H04R 1/46 - Special adaptations for use as contact microphones, e.g. on musical instrument, on stethoscope
H04R 1/40 - Arrangements for obtaining desired frequency or directional characteristics for obtaining desired directional characteristic only by combining a number of identical transducers
H04R 1/04 - Structural association of microphone with electric circuitry therefor
G10L 25/66 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination for extracting parameters related to health condition
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Embodiments of the present disclosure provide systems and methods for diagnosing LVH based on a user's ECG data as well as blood pressure data. The user may record their ECG and blood pressure data using any appropriate ECG and blood pressure monitors, and may augment the ECG and blood pressure data with user characteristics such as user age, sex, diet, and previous medical history before transmitting the ECG and blood pressure data to a cloud storage system. A machine learning (ML) model implemented in the cloud storage system may analyze ECG data of the user using LVH diagnosis criteria, and augment the results of the ECG data analysis with the blood pressure data of the user to form a diagnosis. The diagnosis may indicate whether the user is suffering from LVH, as well as a severity of the LVH.
Embodiments of the present disclosure provide systems and methods for diagnosing LVH based on a user's ECG data as well as blood pressure data. The user may record their ECG and blood pressure data using any appropriate ECG and blood pressure monitors, and may augment the ECG and blood pressure data with user characteristics such as user age, sex, diet, and previous medical history before transmitting the ECG and blood pressure data to a cloud storage system. A machine learning (ML) model implemented in the cloud storage system may analyze ECG data of the user using LVH diagnosis criteria, and augment the results of the ECG data analysis with the blood pressure data of the user to form a diagnosis. The diagnosis may indicate whether the user is suffering from LVH, as well as a severity of the LVH.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
Described herein is an apparatus for monitoring various physiological parameters of a user. The apparatus may be in the form of a commode, so as to allow a user to obtain e.g., an ECG measurement while they are using the commode. The apparatus may comprise an electrode assembly comprising a set of electrodes to perform an electrocardiogram (ECG) by sensing an electrical signal corresponding to heart activity of a user when in contact with skin of the user and output the electrical signal. The apparatus may further comprise a converter assembly to convert the electrical signal to a modulated signal, and a transmitter to transmit the modulated signal. The apparatus may transmit the modulated signal to a computing device which may receive the modulated signal and determine whether the electrical signal indicates that the user is experiencing a heart condition.
Described herein is an apparatus for monitoring various physiological parameters of a user. The apparatus may be in the form of a commode, so as to allow a user to obtain e.g., an ECG measurement while they are using the commode. The apparatus may comprise an electrode assembly comprising a set of electrodes to perform an electrocardiogram (ECG) by sensing an electrical signal corresponding to heart activity of a user when in contact with skin of the user and output the electrical signal. The apparatus may further comprise a converter assembly to convert the electrical signal to a modulated signal, and a transmitter to transmit the modulated signal. The apparatus may transmit the modulated signal to a computing device which may receive the modulated signal and determine whether the electrical signal indicates that the user is experiencing a heart condition.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/26 - Bioelectric electrodes therefor maintaining contact between the body and the electrodes by the action of the subjects, e.g. by placing the body on the electrodes or by grasping the electrodes
Embodiments of the present disclosure provide a small form factor ECG monitoring device that can acquire 3 standard ECG leads including a V-lead, does not require the use of adhesives for electrodes, and provides ECG data for a user on a near instantaneous basis. The ECG monitoring device can acquire leads I, II, and V2 (or any other V lead). The addition of a V-lead not only provides an additional channel of ECG data, but also adds another orthogonal cardiac field plane (the horizontal plane) thanks to the reference point formed by leads I and II. The ECG monitoring device may derive the augmented limb leads and subsequently generate a full 12-lead ECG. The ECG monitoring device may generate one or more diagnoses based on the full 12-lead set. The ECG monitoring device may provide an easy and non-invasive way for a person to take an ECG on the fly.
Embodiments of the present disclosure provide a small form factor ECG monitoring device that can acquire 3 standard ECG leads including a V-lead, does not require the use of adhesives for electrodes, and provides ECG data for a user on a near instantaneous basis. The ECG monitoring device can acquire leads I, II, and V2 (or any other V lead). The addition of a V-lead not only provides an additional channel of ECG data, but also adds another orthogonal cardiac field plane (the horizontal plane) thanks to the reference point formed by leads I and II. The ECG monitoring device may derive the augmented limb leads and subsequently generate a full 12-lead ECG. The ECG monitoring device may generate one or more diagnoses based on the full 12-lead set. The ECG monitoring device may provide an easy and non-invasive way for a person to take an ECG on the fly.
Described herein are devices, systems, and methods for sensing, generating, and time aligning one or more of the 12 leads of a standard ECG. A set of electrodes may be used to perform an electrocardiogram (ECG) of a user to generate a set of limb leads including a first lead I. A Wilson's Central Terminal (WCT) value may be determined using a subset of the set of limb leads. In response to an electrode of the set of electrodes contacting the user's chest, a precordial lead may be generated based on electric potentials sensed by the electrode while contacting the user's chest and the WCT value. As the precordial lead is generated, a corresponding second lead I is simultaneously generated. The corresponding second lead I is used to time align the set of limb leads and the precordial lead.
Described herein are software platforms (or “platforms”), systems, devices, and methods for providing effective healthcare to a patient while increasing the efficiency of the healthcare provider. A platform provides effective healthcare to a patient through, but not limited to, monitoring of the physical parameters and therapeutic objectives of the patient. In addition, a platform is further configured to provide a communication link between a patient and a healthcare provider. The platform may be implemented by a system comprising one or more computing devices that interact with or run one or more applications of the platform.
G08B 21/04 - Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
09 - Scientific and electric apparatus and instruments
Goods & Services
Downloadable computer software applications for medical use in monitoring and analyzing patient physiological activity and in transmitting physiological health data to patient's healthcare providers; Downloadable software applications for monitoring, and transmitting, and analyzing personal EKG readings, cardiac symptoms, and physiological activity
Described herein are methods, systems, and software for monitoring blood pressure. In some embodiments, a using a mobile device is used. The blood pressure monitoring system utilizes heart rate measurements at two separate locations on the body to calculate a differential pulse arrival time which is used to estimate blood pressure. The heart rate measurements can be taken simultaneously, or they can be taken sequentially while simultaneously taking ECG measurement. If taken sequentially, the heart rate measurements are aligned with the ECG to determine the differential. Accurate, inexpensive, and discreet blood pressure monitoring is thus provided.
09 - Scientific and electric apparatus and instruments
10 - Medical apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware, downloadable and recorded software, and peripheral devices for use with mobile computer systems and smartphones, all for medical use in monitoring and analyzing patient physiological activity and in transmitting physiological health data to patient's healthcare providers; downloadable and recorded computer software for use with mobile computer systems, smartphones, and peripheral devices, all for medical use in monitoring, recording, and analyzing personal physiological activity, analyzing ECG readings, and in transmitting physiological health data EKG monitoring apparatus; Portable EKG and heart rate monitors; portable medical apparatus for testing and monitoring heart rate, vital signs, cardiac events and biometric data; medical apparatus and testing instruments for monitoring heart rate, vital signs, and cardiac events with integrated operating software; testing apparatus for medical purposes, namely, personal testing apparatus for heart rate, vital signs, cardiac events, and cardiac reaction to other stimuli; medical devices for monitoring heart rate, vital signs, and cardiac events and downloadable software for recording, processing and transmitting medical data, for use therewith, sold as a unit; carrying cases fitted for portable medical apparatus Software as a Service (SaaS) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring and analyzing patient physiological activity and in transmitting and storing physiological health data
Embodiments of the present disclosure provide a mobile electrocardiogram (ECG) sensor comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The ECG sensor further comprises a processing device, operatively coupled to the electrode assembly, the processing device to provide the sensed heart-related signals to a machine learning module trained to predict a twelve-lead QT interval (QTc) value from the mobile ECG sensor comprising less than twelve leads. The ECG sensor also comprises a housing containing the electrode assembly and the processing device.
Embodiments of the present disclosure provide a mobile electrocardiogram (ECG) sensor comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The ECG sensor further comprises a processing device, operatively coupled to the electrode assembly, the processing device to provide the sensed heart-related signals to a machine learning module trained to predict a twelve-lead QT interval (QTc) value from the mobile ECG sensor comprising less than twelve leads. The ECG sensor also comprises a housing containing the electrode assembly and the processing device.
The embodiments presented herein provide a device comprising a sensor assembly configured to sense physiological signals corresponding to physiological parameters of a user when in contact with the user, and to produce electrical signals representing the sensed physiological signals. The device may also comprise a converter assembly that is electrically connected to the sensor assembly, and is configured to convert the electrical signals to a modulated signal. The device may further comprise a transmitter that transmits the modulated signal wirelessly to a computing device. The device may include a housing that contains the sensor assembly, the converter assembly, and the transmitter.
Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Software as a Service (SaaS) services featuring software for developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring, recording, and analyzing personal physiological activity, analyzing ECG readings, and transmitting and analyzing physiological health data Providing health information in the nature of EKG and heart rate readings and biometric data; providing health information in the nature of EKG and heart rate readings and biometric data by means of online software applications
Apparatuses and methods for extracting, de-noising, and analyzing electrocardiogram signals. Any of the apparatuses described herein may be implemented as a (or as part of a) computerized system. For example, described herein are apparatuses and methods of using them or performing the methods, for extracting and/or de-noising ECG signals from a starting signal. Also described herein are apparatuses and methods for analyzing an ECG signal, for example, to generate one or more indicators or markers of cardiac fitness, including in particular indicators of atrial fibrillation. Described herein are apparatuses and method for determining if a patient is experiencing a cardiac event, such as an arrhythmia.
Described herein are devices, systems, and methods for sensing and generating a one or more of the 12 leads of a standard ECG with a mobile computing device. In some embodiments, three electrodes are positioned on a mobile computing device so that an individual may comfortably contact all three sensors at once to generate a six lead ECG which may be displayed on a display of said mobile computing device. In some embodiments, analysis carried out further comprises determining a QRS axis and QRST angle.
Disclosed systems include mobile ECG sensors, systems and methods. Some embodiments provide ECG sensors in a packaging-label form factor that allows a user to contact two electrically isolated electrodes on an exterior of a product packaging to measure heart electrical signals for a Lead I ECG.
Embodiments of the present disclosure are directed to an electrocardiogram (ECG) monitoring device embedded into the construct of a controller (e.g., video game controller, steering wheel etc.) to monitor a user's heart health and diagnose various conditions (e.g., AFib, tachycardia, bradycardia, etc.). The controller may comprise a set of electrodes including electrodes to contact the user's hands and one or more electrodes on the back of the controller that can be used to contact the user's extremities as a 3rd contact point to provide additional leads for higher accuracy ECG sensing. The set of electrodes may be positioned at locations on the controller where the user's hands are relatively stable, thus minimizing motion artifacts caused by muscular movement (thereby allowing for signal stability and longevity).
An apparatus includes sporting equipment, a sensor assembly, and a processing device. The sensor assembly includes a first sensor and a second sensor that are integrated into the sporting equipment to contact skin of a user. The sensor assembly produces electrical signals indicative of physiological signals of the user. The processing device generates an electrocardiogram (ECG) based on the electrical signals.
An apparatus includes an electrocardiograph device having first, second, and third, electrode assemblies with first, second, and third electrodes adapted to measure first, second, and third electrical signals of an individual, respectively. The apparatus further includes a processing device to: determine a Lead I from the first electrical signal and the second electrical signal; determine a Lead II from the second electrical signal and the third electrical signal; generate a Lead III using (Lead III = Lead II - Lead I); determine, using a machine learning model trained using measured twelve-lead ECG data, Leads aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6 based on Lead I, Lead II, and Lead III; and provide Leads Lead I, Lead II, Lead III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6 for display on a client device.
An apparatus includes an electrocardiograph device having first, second, and third, electrode assemblies with first, second, and third electrodes adapted to measure first, second, and third electrical signals of an individual, respectively. The apparatus further includes a processing device to: determine a Lead I from the first electrical signal and the second electrical signal; determine a Lead II from the second electrical signal and the third electrical signal; generate a Lead III using (Lead III=Lead II−Lead I); determine, using a machine learning model trained using measured twelve-lead ECG data, Leads aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6 based on Lead I, Lead II, and Lead III; and provide Leads Lead I, Lead II, Lead III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6 for display on a client device.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A mobile electrocardiogram (ECG) device is described, comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The mobile ECG device further comprises a converter assembly electrically connected to the electrode assembly, configured to convert the electrical signals to a modulated signal, wherein the modulated signal carries the electrical signals representing the sensed heart-related signals. The mobile ECG device further comprises a transmitter that transmits the modulated signal wirelessly to a computing device. The mobile ECG device further comprises a light-emitting device to facilitate an optimal placement of the electrode on the body of the user. The mobile ECG device further comprises a housing containing the electrode assembly, the converter assembly, the transmitter, and the light-emitting device.
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
A61B 5/0245 - Measuring pulse rate or heart rate using sensing means generating electric signals
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
A61B 5/021 - Measuring pressure in heart or blood vessels
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
66.
Devices, systems, and methods for physiology monitoring
Software applications, devices, systems, and methods are provided for monitoring, recording, and tracking cardiac health related metrics using an associated computing device. The software applications may monitor, record, and track physiological data such as cardiac electrical activity, heart rate, and blood pressure. Data transmission between a patient and a healthcare provider may also be enabled via the software applications disclosed herein.
G08B 21/04 - Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware, software, and peripheral devices for use
with mobile computer systems and smartphones, all for
medical and veterinary use in monitoring patient
physiological indicators and in transmitting physiological
health data to patient's healthcare providers. Application service provider services, namely, developing,
hosting, managing, and maintaining applications, software,
web services, and websites, all for medical and veterinary
use in monitoring patient physiological indicators and in
transmitting and storing physiological health data.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware, software, and peripheral devices for use
with mobile computer systems and smartphones, all for
medical use in monitoring and analyzing patient
physiological activity and in transmitting physiological
health data to patient's healthcare providers. Software as a Service (SaaS) services, namely, developing,
hosting, managing, and maintaining applications, software,
web services, and websites, all for medical use in
monitoring and analyzing patient physiological activity and
in transmitting and storing physiological health data.
EKG monitoring apparatus; Portable EKG and heart rate monitors; portable medical apparatus for testing and monitoring heart rate, vital signs, cardiac events and biometric data; medical apparatus and testing instruments for monitoring heart rate, vital signs, and cardiac events with integrated operating software; testing apparatus for medical purposes, namely, personal testing apparatus for heart rate, vital signs, cardiac events, and cardiac reaction to other stimuli; medical devices for monitoring heart rate, vital signs, and cardiac events and downloadable software for recording, processing and transmitting medical data, for use therewith, sold as a unit; carrying cases fitted for portable medical apparatus
EKG monitoring apparatus; Portable EKG and heart rate monitors; portable medical apparatus for testing and monitoring heart rate, vital signs, cardiac events and biometric data; medical apparatus and testing instruments for monitoring heart rate, vital signs, and cardiac events with integrated operating software; testing apparatus for medical purposes, namely, personal testing apparatus for heart rate, vital signs, cardiac events, and cardiac reaction to other stimuli; medical devices for monitoring heart rate, vital signs, and cardiac events and downloadable software for recording, processing and transmitting medical data, for use therewith, sold as a unit; carrying cases fitted for portable medical apparatus
71.
Methods and systems for arrhythmia tracking and scoring
A dashboard centered around arrhythmia or atrial fibrillation tracking is provided. The dashboard includes a heart or cardiac health score that can be calculated in response to data from the user such as their ECG and other personal information and cardiac health influencing factors. The dashboard also provides to the user recommendations or goals, such as daily goals, for the user to meet and thereby improve their heart or cardiac health score. These goals and recommendations may be set by the user or a medical professional and routinely updated as his or her heart or cardiac health score improves or otherwise changes. The dashboard is generally displayed from an application provided on a smartphone or tablet computer of the user.
A61B 5/0245 - Measuring pulse rate or heart rate using sensing means generating electric signals
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
Described herein are systems, devices, and methods for cardiac monitoring. In particular, the systems, devices, and methods described herein may be used to conveniently sense the presence of an intermittent arrhythmia in an individual. The systems, devices, and methods described herein may be further configured to sense an electrocardiogram.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Computer hardware, software, and peripheral devices for use with mobile computer systems and smartphones, all for medical and veterinary use in monitoring patient physiological indicators and in transmitting physiological health data to patient's healthcare providers. (1) Application service provider services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical and veterinary use in monitoring patient physiological indicators and in transmitting and storing physiological health data.
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Software as a service (SaaS) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring and analyzing patient physiological activity and in transmitting and storing physiological health data.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Computer hardware, software, and peripheral devices for use with mobile computer systems and smartphones, all for medical use in monitoring and analyzing patient physiological activity and in transmitting physiological health data to patient's healthcare providers. (1) Software as a Service (SaaS) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring and analyzing patient physiological activity and in transmitting and storing physiological health data.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a service (SaaS) services, namely, developing,
hosting, managing, and maintaining applications, software,
web services, and websites, all for medical use in
monitoring and analyzing patient physiological activity and
in transmitting and storing physiological health data.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable and recorded computer software for use with mobile computer systems, smartphones, and associated computer hardware and peripheral devices, for use in monitoring and analyzing personal physiological activity and health data, sharing and transmitting it to family, friends, caregivers, and personal healthcare providers, and managing the data and who may receive it Providing online, non-downloadable computer software for use in monitoring and analyzing personal physiological activity and health data, sharing and transmitting data to family, friends, caregivers, and personal healthcare providers, and managing the data and who may receive it; Software as a Service (SaaS) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for use in monitoring and analyzing personal physiological activity and health data, sharing and transmitting it to family, friends, caregivers, and personal healthcare providers, and managing the data and who may receive it
Apparatuses and methods for extracting, de-noising, and analyzing electrocardiogram signals. Any of the apparatuses described herein may be implemented as a (or as part of a) computerized system. For example, described herein are apparatuses and methods of using them or performing the methods, for extracting and/or de-noising ECG signals from a starting signal. Also described herein are apparatuses and methods for analyzing an ECG signal, for example, to generate one or more indicators or markers of cardiac fitness, including in particular indicators of atrial fibrillation. Described herein are apparatuses and method for determining if a patient is experiencing a cardiac event, such as an arrhythmia.
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health indicator ("other-factors") to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
80.
MACHINE LEARNING HEALTH ANALYSIS WITH A MOBILE DEVICE
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health indicator ("other-factors") to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
A61B 5/0452 - Detecting specific parameters of the electrocardiograph cycle
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
Described herein are devices, systems, and methods for sensing and generating a one or more of the 12 leads of a standard ECG with a mobile computing device. In some embodiments, three electrodes are positioned on a mobile computing device so that an individual may comfortably contact all three sensors at once to generate a six lead ECG which may be displayed on a display of said mobile computing device. In some embodiments, analysis carried out further comprises determining a QRS axis and QRST angle.
A dashboard centered around arrhythmia or atrial fibrillation tracking is provided. The dashboard includes a heart or cardiac health score that can be calculated in response to data from the user such as their ECG and other personal information and cardiac health influencing factors. The dashboard also provides to the user recommendations or goals, such as daily goals, for the user to meet and thereby improve their heart or cardiac health score. These goals and recommendations may be set by the user or a medical professional and routinely updated as his or her heart or cardiac health score improves or otherwise changes. The dashboard is generally displayed from an application provided on a smartphone or tablet computer of the user.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 40/67 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/0452 - Detecting specific parameters of the electrocardiograph cycle
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
Disclosed systems include mobile ECG sensors, systems and methods. Some embodiments provide ECG sensors in a credit card form factor that allows a user to contact two electrically isolated electrodes to measure heart electrical signals for a Lead 1 ECG.
Disclosed systems and method receive electrocardiogram (EKG) data of a subject. The EKG data comprises a record of at least a full beat of the subject. The EKG data is input into a machine learning model to generate an output. The output can include a segmentation of the full beat or a measurement of a QT interval of the subject.
Disclosed systems include mobile ECG sensors, systems and methods. Some embodiments provide ECG sensors in a credit card form factor that allows a user to contact two electrically isolated electrodes to measure heart electrical signals for a Lead I ECG.
Devices, systems, and methods for measuring and monitoring biometric or physiological parameters in a user-friendly and convenient manner are disclosed. Relevant physiological parameters of the user may be measured as the user normally operates a computing device or other hand-operated or hand-held device. These parameters are measured using an accessory of the device such as a laptop case, a tablet computer case, a smartphone case, or a smart watch or smart armband. The accessory may include at least two or three electrodes for taking an electrocardiogram or other physiological parameters. The measured parameters are transmitted to the computing device. The computing device can be normally used while a physiological parameter monitoring and measurement application loaded onto the computing device operates in the background to receive the measured parameters.
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health indicator ("other-factors") to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
G16H 40/60 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
90.
Continuous monitoring of a user's health with a mobile device
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/0452 - Detecting specific parameters of the electrocardiograph cycle
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
Described herein are systems, devices, and methods for cardiac monitoring. In particular, the systems, devices, and methods described herein may be used to conveniently sense the presence of an intermittent arrhythmia in an individual. The systems, devices, and methods described herein may be further configured to sense an electrocardiogram.
Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (“other-factors”) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a Service (SaaS) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring and analyzing patient physiological activity and in transmitting and storing physiological health data
94.
Methods and systems for arrhythmia tracking and scoring
A dashboard centered around arrhythmia or atrial fibrillation tracking is provided. The dashboard includes a heart or cardiac health score that can be calculated in response to data from the user such as their ECG and other personal information and cardiac health influencing factors. The dashboard also provides to the user recommendations or goals, such as daily goals, for the user to meet and thereby improve their heart or cardiac health score. These goals and recommendations may be set by the user or a medical professional and routinely updated as his or her heart or cardiac health score improves or otherwise changes. The dashboard is generally displayed from an application provided on a smartphone or tablet computer of the user.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
A61B 5/021 - Measuring pressure in heart or blood vessels
A61B 5/0452 - Detecting specific parameters of the electrocardiograph cycle
A61B 5/11 - Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
Described herein are methods, systems, and software for monitoring blood pressure. In some embodiments, a using a mobile device is used. The blood pressure monitoring system utilizes heart rate measurements at two separate locations on the body to calculate a differential pulse arrival time which is used to estimate blood pressure. The heart rate measurements can be taken simultaneously, or they can be taken sequentially while simultaneously taking ECG measurement. If taken sequentially, the heart rate measurements are aligned with the ECG to determine the differential. Accurate, inexpensive, and discreet blood pressure monitoring is thus provided.
Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 40/63 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
G06N 3/04 - Architecture, e.g. interconnection topology
A61B 5/35 - Detecting specific parameters of the electrocardiograph cycle by template matching
A61B 5/316 - Modalities, i.e. specific diagnostic methods
Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
98.
Systems and methods of analyte measurement analysis
Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.
A61B 5/145 - Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06N 3/04 - Architecture, e.g. interconnection topology
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
G06N 3/044 - Recurrent networks, e.g. Hopfield networks
Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware, software, and peripheral devices for use with mobile computer systems and smartphones, all for medical use in monitoring and analyzing patient physiological activity and in transmitting physiological health data to patient's healthcare providers. Software as a service (saas) services, namely, developing, hosting, managing, and maintaining applications, software, web services, and websites, all for medical use in monitoring and analyzing patient physiological activity and in transmitting and storing physiological health data.
