A computer implemented method and system for detecting premature ventricular contractions (PVCs) are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a cycle length (CL) distribution metric that plots a series of cardiac beats into one of a set of transition types based on R-R interval (RRI) difference pairs associated with the cardiac beats. The CL distribution metric plots the cardiac beats based on a comparison between combinations of the RRI difference pairs for corresponding combinations of the cardiac beats. The method calculates a distribution characteristic for the cardiac beats, from the series of cardiac beats that exhibit a first transition type from the set of transition types and calculates a discrimination score based on the distribution characteristic of the cardiac beats across the CL distribution metric. The method designates the CA signals to include a predetermined level of PVC burden based on the discrimination score.

A computer implemented method and system for detecting premature ventricular contractions (PVCs) are provided. The method is under control of one or more processors configured with specific executable instructions. The method obtains a cycle length (CL) distribution metric that plots a series of cardiac beats into one of a set of transition types based on R-R interval (RRI) difference pairs associated with the cardiac beats. The CL distribution metric plots the cardiac beats based on a comparison between combinations of the RRI difference pairs for corresponding combinations of the cardiac beats. The method calculates a distribution characteristic for the cardiac beats, from the series of cardiac beats that exhibit a first transition type from the set of transition types and calculates a discrimination score based on the distribution characteristic of the cardiac beats across the CL distribution metric. The method designates the CA signals to include a predetermined level of PVC burden based on the discrimination score.

A method for selecting one or more targets for non-invasively treating a cardiac arrhythmia in a patient includes receiving a mapping associated with the patient's heart and generating a segmented model of the mapping associated with the patient's heart. The segmented model divides the mapping into a plurality of segments. The method includes identifying one or more abnormality in the segmented model of the mapping associated with the patient's heart, determining which segment or segments of the plurality of segments include the identified one or more abnormality, and selecting a target for non-invasive treatment of the cardiac arrhythmia based on the determined segment or segments of the plurality of segments that include the identified one or more abnormality.

A method for selecting one or more targets for non-invasively treating a cardiac arrhythmia in a patient includes receiving a mapping associated with the patient's heart and generating a segmented model of the mapping associated with the patient's heart. The segmented model divides the mapping into a plurality of segments. The method includes identifying one or more abnormality in the segmented model of the mapping associated with the patient's heart, determining which segment or segments of the plurality of segments include the identified one or more abnormality, and selecting a target for non-invasive treatment of the cardiac arrhythmia based on the determined segment or segments of the plurality of segments that include the identified one or more abnormality.

A patient-worn arrhythmia monitoring and treatment device includes at least two pads configured to affix to skin on a torso of a patient. At least one of a pair of sensing electrodes is disposed on each one of the pads and configured to sense surface ECG activity of the patient. At least one of a pair of therapy electrodes is disposed on each one of the pads and configured to deliver one or more therapeutic pulses to the patient. A controller is in communication with the pairs of sensing and therapy electrodes and is configured to monitor for cardiac arrhythmias based on the sensed surface ECG activity and cause the delivery of the one or more therapeutic pulses. The device includes a removable garment to be worn about the torso to immobilize on the torso the one of the at least two pads to which the controller is coupled.

A blood pressure (BP) measuring device including a PPG sensor, having one or more light sources and one or more light detectors; a computing unit, including a receiver for receiving PPG signals from the PPG sensor and a sampling circuit, for generating PPG signals samples of the PPG signals, where the device also includes a processor having BP calculation functionality, for processing the PPG signals samples into sequential BP values and a BP output unit, for outputting the calculated BP values, where the sampling circuit is adapted to sample at high sampling rate and provide BP values at a rate higher than 1 BP value per second, where the device may also include an electrogram sensor, having one or more electrodes for outputting tissue electrical activity values, the computing unit is connected to the electrogram sensor.

The exemplified methods and systems facilitate the configuration and training of a neural network (e.g., a deep neural network, a convolutional neural network (CNN), etc.), or ensemble(s) thereof, with a biophysical signal data set to ascertain estimate for the presence or non-presence of disease or pathology in a subject as well as to assess and/or classify disease or pathology, including for example in some cases the severity of such disease or pathology, in a subject. In the context of the heart, the methods and systems described herein facilitate the configuration and training of a neural network, or ensemble(s) thereof, with a cardiac signal data set to ascertain estimate for the presence or non-presence of coronary artery disease or coronary pathology.

Disclosed are a method and apparatus for visualizing an electrocardiogram using deep learning.

The present embodiment provides a method and apparatus for visualizing an electrocardiogram, the method and apparatus which analyze an electrocardiogram using a deep learning algorithm for accurate arrhythmia determination as a real-time operation algorithm for monitoring a bedridden patient in order to solve the manpower shortage of medical staff, and then visually output it in real time so that a visual help may be provided for medical staff.

Disclosed are a method and apparatus for visualizing an electrocardiogram using deep learning.

The present embodiment provides a method and apparatus for visualizing an electrocardiogram, the method and apparatus which analyze an electrocardiogram using a deep learning algorithm for accurate arrhythmia determination as a real-time operation algorithm for monitoring a bedridden patient in order to solve the manpower shortage of medical staff, and then visually output it in real time so that a visual help may be provided for medical staff.

Systems and methods for generating an ablation map identifying target ablation locations on a heart of a patient are provided. One or more input medical images of a heart of a patient and a voltage map of the heart of the patient are received. An ablation map identifying target ablation locations on the heart is generated using one or more trained machine learning based models based on the one or more input medical images and the voltage map. The ablation map is output.