A device for processing spatiotemporal dispersion signals of electrocardiograms. The device comprising a memory configured to receive spatiotemporal dispersion signals of electrocardiograms associated with a time marker and an electrocardiogram track. The device comprising a calculator configured to receive as input an electrogram track identifier and a time marker, to analyse the spatiotemporal dispersion signal of electrocardiograms associated with this electrogram track identifier, analyse a signal extract comprised between the time marker and the first previous time marker whose spatiotemporal dispersion signal value of electrocardiograms indicates a lack of dispersion, derive from this signal extract a flatness value of the signal, and a duration value derived from the duration of the signal extract, and feed back a track priority value calculated from the flatness value and the duration value. The device comprising a monitor configured to receive the spatiotemporal dispersion signals of electrocardiograms associated with each electrocardiogram track.
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
Zii Yii YCii Yii i in said atrial tachycardia class hierarchy, and N is the total number of classifiers, said device being arranged to receive an input data set, to process it with the input module, to derive therefrom a set of assessments provided to said Bayesian engine, and to return one or more atrial tachycardia information derived from said updated set of assessments.
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
3.
COMPUTER DEVICE FOR REAL-TIME ANALYSIS OF ELECTROGRAMS AND METHOD
A computer device for analysis of electrograms comprises a data storage (114) arranged to receive electrogram signals each originating from one of a plurality of electrodes of a catheter and catheter electrode distance data, a random convolutional kernel-based feature extractor arranged to receive electrogram signals associated with the electrodes of a catheter for a given timecode and to return an electrode feature vector for each electrogram signal, and an input generator arranged to receive the electrogram feature vectors output by said random convolutional kernel-based feature extractor and catheter electrode distance data relating to the catheter from which these electrogram signals originate, to associate each electrode with at least the three closest electrodes of the catheter based on said catheter electrode distance data, and to return an input vector in which the electrode feature vectors of each electrode is grouped with the electrode feature vectors of its associated electrodes and the corresponding catheter electrode distance data, a gradient-boosted based machine learning module trained on data comprising sets of input vectors labelled with a value indicating for each electrode if its electrogram signal exhibits spatiotemporal dispersion. The computer device is arranged to receive electrogram signals associated and electrode distance date associated with a timecode, to feed them to said input generator, to use the resulting input vectors as input for said gradient-boosted based machine learning module, and to return a dispersion value for each electrode.
A61B 5/367 - Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/287 - Holders for multiple electrodes, e.g. electrode catheters for electrophysiological study [EPS]
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 device for processing cardiac signals including a memory receiving input data sets including a plurality of P wave segments associated with an electrocardiogram track and with an acquisition time window, and a plurality of coronary sinus signals associated with the same acquisition time window and having one or more activation sequence(s). The device includes an extractor arranged, for a given input data set, to determine for at least some P wave segments of the given input data set a wave polarity profile type, at least one extremum feature. The extractor is also arranged to determine, for each group of track P wave data, a set of track P wave features comprising a polarity profile type, a data set extremum feature value for each calculated extremum feature type and an integral value determined based on the data of the corresponding group of track P wave data.
A device for determining a cardiac activation cycle length including a memory arranged so as to store electrogram data having time stamps and associated with a channel. The device includes a preparer arranged so as to receive electrogram data associated with a given channel and with a time window of at least 1.5 seconds. The device includes a detector arranged so as to receive the pre-processed data and to detect therein non-overlapping activation segments that each correspond to a window within said time window of at least 1.5 seconds. The device includes a computer arranged so as to determine a periodicity condition of the activations by determining in each activation segment a reference time point and by comparing the duration of intervals each defined by two reference time points consecutive to the duration of the time window of at least 1.5 seconds.
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
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A computer implemented heart surgery assistance method comprises: a) receiving (300) one or more procedure images representing the heart a patient, said one or more procedure images forming a three-dimensional map associating heart-condition values derived from electrical measurements of said heart a patient to the location at which those electrical measurements were obtained, b) determining or receiving (310) ablation-candidate points derived from said heart-condition values in said at least one or more procedure images based on said electrical measurements, c) determining (320) cluster boundaries within said at least one or more procedure images by regrouping ablation-candidate points which are less than 4mm apart within said three-dimensional map, d) determining (330-380) ablation lines joining neighboring cluster boundaries and/or ablation-candidate points which are less than 4mm apart within said three-dimensional map, e) outputting (390) to a display said three-dimensional map along with data representing said cluster boundaries, said ablation lines, and at least said ablation-candidate points which are neither contained with a cluster boundary or within an ablation line.
A device for processing cardiac signals, comprises: - a data storage (114) arranged to receive input data sets each comprising a plurality of P wave segments each associated with an electrocardiogram track and with an acquisition time window, and a plurality of coronary sinus signals associated with the same acquisition time window and having one or more activation sequence(s), - a random convolutional kernel-based extractor arranged, for a given input data set, to determine an electrocardiogram feature vector, and - a machine-learning based locator using decision trees trained on data comprising sets of electrocardiogram feature vectors labelled with a value indicating a cardiac region identifier, and arranged to receive an electrocardiogram feature vector from the random convolutional kernel-based extractor as input, and to return a cardiac region identifier as output.
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
8.
COMPUTER DEVICE FOR REAL-TIME ANALYSIS OF ELECTROGRAMS
A computer device for analysis of electrograms, comprises a data storage (114) arranged to receive electrogram signals each originating from one of a plurality of electrodes of a catheter and catheter electrode distance data, a random convolutional kernel-based feature extractor arranged to receive electrogram signals associated with the electrodes of a catheter for a given timecode and to return an electrode feature vector for each electrogram signal, and an input generator arranged to receive the electrogram feature vectors output by said random convolutional kernel-based feature extractor and catheter electrode distance data relating to the catheter from which these electrogram signals originate, to associate each electrode with at least the three closest electrodes of the catheter based on said catheter electrode distance data, and to return an input vector in which the electrode feature vectors of each electrode is grouped with the electrode feature vectors of its associated electrodes and the corresponding catheter electrode distance data, a gradient-boosted based machine learning module trained on data comprising sets of input vectors labelled with a value indicating for each electrode if its electrogram signal exhibits spatiotemporal dispersion. The computer device is arranged to receive electrogram signals associated and electrode distance date associated with a timecode, to feed them to said input generator, to use the resulting input vectors as input for said gradient-boosted based machine learning module, and to return a dispersion value for each electrode.
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
The invention relates to a device for processing intracardiac signals, which comprises a memory unit (4) arranged to receive electrocardiogram data and synchronised electrogram data, a detector (6) arranged to analyse the electrocardiogram data and to detect QRS wave instants therein, an analyser (8) arranged to perform a wavelet transform of the electrogram data, an extractor (10) arranged to collect coefficients from the wavelet transform, each associated with a QRS wave instant detected by the detector (6), and to store them in a buffer (14), and a composer (12) arranged to extract a QRS fingerprint signal from the buffer (14) and subtract it from the wavelet transform at the QRS wave instants, and to output denoised electrogram data by inverse wavelet transform of the resulting signal.
A61B 5/349 - Detecting specific parameters of the electrocardiograph cycle
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
10.
DISPOSITIF DE TRAITEMENT DE SIGNAUX DE DISPERSION SPATIOTEMPORELLE D'ELECTROGRAMMES CARDIAQUES
The inventions described herein relate to systems and methods directed to data-driven, continuous, and adaptable learning approaches to analyzing atrial tachycardia (AT) in a human body. The systems and methods may create an AT profile that automatically evolves such that a subsequent change in the AT is more accurately recognized and categorized.
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
A device for processing cardiac electrogram spatiotemporal dispersion signals comprises a memory (4) arranged to receive cardiac electrogram spatiotemporal dispersion signals associated, on the one hand, with a time marker and, on the other hand, with a cardiac electrogram trace, a computer (8) arranged to receive, as input, an electrogram trace identifier and a time marker to analyse the cardiac electrogram spatiotemporal dispersion signal associated with this electrogram trace identifier, to analyse a signal extract between the time marker and the first preceding time marker of which the cardiac electrogram spatiotemporal dispersion signal value indicates an absence of dispersion, to obtain from this signal extract, on the one hand, a flatness value of the signal and, on the other hand, a duration value derived from the duration of the signal extract, and to return a trace priority value calculated from the flatness value and the duration value, and a monitor (6) arranged to receive the cardiac electrogram spatiotemporal dispersion signals associated with each cardiac electrogram trace, and, when the value of a cardiac electrogram spatiotemporal dispersion signal indicates a relevant dispersion, to call the computer (8) with the corresponding time marker and the corresponding electrogram trace identifier.
A device for processing spatiotemporal dispersion signals of cardiac electrograms comprises a memory (4) designed to receive spatiotemporal dispersion signals of cardiac electrograms, cardiac cycle length values and voltage histogram uniformity values each associated, on the one hand, with a time marker and, on the other hand, with a cardiac electrogram trace identifier. The device comprises a monitor (6) which detects the presence of a relevant spatiotemporal dispersion signal on a trace and which, in response thereto, calls a computer (8) which calculates a first priority value from the spatiotemporal dispersion signal, a second priority value from the associated cardiac cycle length value or values, and a third priority value from the associated voltage histogram uniformity values and then draws a trace priority value from these three priority values.
The invention relates to a device for processing cardiac signals comprising a memory (4) for receiving input datasets comprising a plurality of P-wave segments associated with an electrocardiogram track and an acquisition time window, and a plurality of coronary sinus signals associated with the same acquisition time window having one or more activation sequences, an extractor (6) arranged, for a given input dataset, to determine, for at least some of the P-wave segments of the given input dataset, a type of wave polarity profile, at least one extreme characteristic of a type chosen from a group of types comprising the number of positive local extrema, the number of negative local extrema, the positive prominence maximum and the negative prominence maximum, and at least one integral value of these P-wave segments, and to combine the resulting data into a group of track P-wave data according to the electrocardiogram track with which each P-wave segment is associated from which the resulting data have been calculated. The extractor (6) is also arranged to determine, for each group of track P-wave data, a set of track P-wave characteristics comprising a type of polarity profile, an extremum characteristic value of the dataset for each calculated extremum characteristic type and an integral value determined on the basis of the data of the corresponding track P-wave data group. The extractor is also arranged to determine activation times in at least some of the activation sequences of the coronary sinus signals of the given input dataset and to derive a set of time values therefrom, and to return a set of dataset characteristics comprising, on the one hand, the set of time values and, on the other hand, the sets of track P-wave characteristics. The device also comprises a machine learning-based locator using decision trees arranged to receive, as input, a set of dataset characteristics and to return, as output, a cardiac region identifier.
A computer device for real-time analysis of electrograms, comprising a memory arranged to receive real-time electrograms signals each originating from one of a plurality of electrodes, a first evaluator comprising an extractor and a gradient boosting based machine learning module, said extractor being arranged to extract a set of features comprising at least one timewise analysis feature and at least one morphological feature from each electrogram signal within a set of electrogram signals, and to feed the resulting sets of features to said gradient boosting based machine learning module trained on data comprising sets of features labelled with a value indicating whether the associated electrogram signal exhibits dispersion and arranged to output for each set of electrogram.
The invention relates to a device for determining a length of a cardiac activation cycle, said device comprising: a memory (4) which is arranged to store electrocardiogram data which have time stamps and are associated with a channel; a preparer (6) which is arranged to receive electrocardiogram data which are associated with a given channel and with a time window of at least 1.5 seconds in order to extract the baseline noise and the high-frequency noise therefrom and to provide pre-processed data; a detector (10) arranged to receive the pre-processed data and to detect therein activation segments with no overlap, each of which corresponds to a window within said time window of at least 1.5 seconds, which detector (10) operates by determining local extremes in the pre-processed data and by grouping them together into activation segments; and a computer (12) which is arranged to determine a periodicity condition of the activations by determining, in each activation segment, a reference time and by comparing the duration of intervals, each defined by two successive reference times, to the duration of the time window of at least 1.5 seconds and, in the event of a periodicity condition indicating periodic activation segments, to determine an activation cycle length from the mean or median of the interval durations.
The invention relates to a device for processing intracardiac signals, which comprises a memory unit (4) arranged to receive electrocardiogram data and synchronised electrogram data, a detector (6) arranged to analyse the electrocardiogram data and to detect QRS wave instants therein, an analyser (8) arranged to perform a wavelet transform of the electrogram data, an extractor (10) arranged to collect coefficients from the wavelet transform, each associated with a QRS wave instant detected by the detector (6), and to store them in a buffer (14), and a composer (12) arranged to extract a QRS fingerprint signal from the buffer (14) and subtract it from the wavelet transform at the QRS wave instants, and to output denoised electrogram data by inverse wavelet transform of the resulting signal.
The invention relates to a device for processing intracardiac signals, which comprises a memory unit (4) arranged to receive electrocardiogram data and synchronised electrogram data, a detector (6) arranged to analyse the electrocardiogram data and to detect QRS wave instants therein, an analyser (8) arranged to perform a wavelet transform of the electrogram data, an extractor (10) arranged to collect coefficients from the wavelet transform, each associated with a QRS wave instant detected by the detector (6), and to store them in a buffer (14), and a composer (12) arranged to extract a QRS fingerprint signal from the buffer (14) and subtract it from the wavelet transform at the QRS wave instants, and to output denoised electrogram data by inverse wavelet transform of the resulting signal.
The inventions described herein relate to systems and methods directed to data-driven, continuous, and adaptable learning approaches to analyzing atrial tachycardia (AT) in a human body. The systems and methods may create an AT profile that automatically evolves such that a subsequent change in the AT is more accurately recognized and categorized.
G06F 18/213 - Feature extraction, e.g. by transforming the feature spaceSummarisationMappings, e.g. subspace methods
G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
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
23.
ATRIAL TACHYCARDIA ANALYSIS MODULE PROVIDING EVOLVING BLUEPRINT OF TACHYCARDIA EVENTS
The inventions described herein relate to systems and methods directed to data-driven, continuous, and adaptable learning approaches to analyzing atrial tachycardia (AT) in a human body. The systems and methods may create an AT profile that automatically evolves such that a subsequent change in the AT is more accurately recognized and categorized.
A61B 5/367 - Electrophysiological study [EPS], e.g. electrical activation mapping or electro-anatomical mapping
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 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
A computer device for real-time analysis of electrograms, comprising a memory arranged to receive real-time electrograms signals each originating from one of a plurality of electrodes, a first evaluator comprising an extractor and a gradient boosting based machine learning module, said extractor being arranged to extract a set of features comprising at least one timewise analysis feature and at least one morphological feature from each electrogram signal within a set of electrogram signals, and to feed the resulting sets of features to said gradient boosting based machine learning module trained on data comprising sets of features labelled with a value indicating whether the associated electrogram signal exhibits dispersion and arranged to output for each set of electrogram.
A device for detecting heart rhythm disorders comprises a memory designed to receive cardiac electrogram data and to store data defining a first classification model for detecting heart rhythm disorders and a second classification model for detecting heart rhythm disorders, a classifier designed to analyze cardiac electrogram data based on a classification model, and to return a classification value, and a driver designed to store cardiac electrogram data in the memory and analyze the data with the classifier, and to return alert data when the analysis by the classifier returns a classification value associated with a heart rhythm disorder.
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
26.
COMPUTING DEVICE FOR DETECTING HEART RHYTHM DISORDERS
A device for detecting heart rhythm disorders comprises a memory (10) designed to receive cardiac electrogram data and to store data defining a first classification model for detecting heart rhythm disorders and a second classification model for detecting heart rhythm disorders, a classifier (6) designed to analyse cardiac electrogram data based on a classification model, and to return a classification value, and a driver (8) design to store cardiac electrogram data in the memory and analyse them with the classifier, and to return alert data when the analysis by the classifier returns a classification value associated with a heart rhythm disorder.
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
27.
COMPUTING DEVICE FOR DETECTING HEART RHYTHM DISORDERS
A device for detecting heart rhythm disorders comprises a memory (10) designed to receive cardiac electrogram data and to store data defining a first classification model for detecting heart rhythm disorders and a second classification model for detecting heart rhythm disorders, a classifier (6) designed to analyse cardiac electrogram data based on a classification model, and to return a classification value, and a driver (8) design to store cardiac electrogram data in the memory and analyse them with the classifier, and to return alert data when the analysis by the classifier returns a classification value associated with a heart rhythm disorder.
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 5/042 - Electrodes specially adapted therefor for introducing into the body
28.
VOLTA MEDICAL ARTIFICIAL INTELLIGENCE SERVING HEART RHYTHM
09 - Scientific and electric apparatus and instruments
10 - Medical apparatus and instruments
38 - Telecommunications services
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Software [recorded programs]; data processing apparatus;
electric regulating apparatus; electric measuring
apparatus; diagnostic apparatus not for medical use;
computer hardware; intercommunication apparatus; interfaces
[for computers]; robots with artificial intelligence. Testing apparatus for medical use; diagnostic apparatus for
medical use; surgical apparatus and instruments;
electrocardiographs; electrodes for medical use; heart
pacemakers; veterinary apparatus and instruments. Provision of access to databases. Computer system analysis; computer system design; software
development [design]; software as a service [SaaS]; rental
of computer software; scientific research. Health care services; medical assistance; veterinary
assistance; health counseling services; medical equipment
rental; hospital services; telemedicine services.
29.
Regional high-density mapping of the atrial fibrillation substrate
The present invention concerns a method for identifying areas of the heart of a patient able to be involved in the perpetuation of atrial fibrillation. This method takes into account the reference cycle of the arrhythmia and has two variants: a local variant in which the areas of the heart are each analysed separately and a regional variant in which several areas of the heart are analysed together. The invention also concerns device for implementing said method a program and the medium thereof.
The present invention concerns a method for identifying areas of the heart of a patient able to be involved in the perpetuation of atrial fibrillation. This method takes into account the reference cycle of the arrhythmia and has two variants: a local variant in which the areas of the heart are each analysed separately and a regional variant in which several areas of the heart are analysed together. The invention also concerns a device for implementing said method, a program and the medium thereof.
