Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

Systems are provided for generating data representing electromagnetic states of a heart for medical, scientific, research, and/or engineering purposes. The systems generate the data based on source configurations such as dimensions of, and scar or fibrosis or pro-arrhythmic substrate location within, a heart and a computational model of the electromagnetic output of the heart. The systems may dynamically generate the source configurations to provide representative source configurations that may be found in a population. For each source configuration of the electromagnetic source, the systems run a simulation of the functioning of the heart to generate modeled electromagnetic output (e.g., an electromagnetic mesh for each simulation step with a voltage at each point of the electromagnetic mesh) for that source configuration. The systems may generate a cardiogram for each source configuration from the modeled electromagnetic output of that source configuration for use in predicting the source location of an arrhythmia.

A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.

Methods and systems for identifying optimized ablation targets for treating and preventing arrhythmias sustained by reentrant circuits are described. The methods comprise receiving at least one mesh generated from one or more images of a patient's heart, receiving activation data generated from one or more simulations of electrical-signal propagation over the at least one mesh, generating at least one flow graph based on the activation data and the at least one mesh, and applying a max-flow min-cut algorithm to the at least one flow graph to determine at least one of a number, one or more dimensions, and one or more locations of one or more ablation targets. Non-transitory computer-readable media storing a set of instructions for treating and preventing arrhythmias sustained by reentrant circuits are also described.

Methods and systems for identifying optimized ablation targets for treating and preventing arrhythmias sustained by reentrant circuits are described. The methods comprise receiving at least one mesh generated from one or more images of a patient's heart, receiving activation data generated from one or more simulations of electrical-signal propagation over the at least one mesh, generating at least one flow graph based on the activation data and the at least one mesh, and applying a max-flow min-cut algorithm to the at least one flow graph to determine at least one of a number, one or more dimensions, and one or more locations of one or more ablation targets. Non-transitory computer-readable media storing a set of instructions for treating and preventing arrhythmias sustained by reentrant circuits are also described.

Wrist-wearable apparatuses that may be removed and used as a chest-applied cardiac device may include two chest electrodes on an inner surface of a strap (or strap regions), as well as two finger or more finger electrodes on the opposite side of the apparatus. The apparatus may be removed from the wrist and placed on a chest of a patient such that two electrodes are spaced at least five centimeters apart and in contact with the chest and held in place with two or more fingers to capture orthogonal cardiac signals that may be synthesized into a conventional 12-lead cardiac signal.

Computer-aided diagnosis algorithms require a large volume of training data. The existing methods for simulating artificial biomedical signals are mostly based on physics driven mathematical models that require too many assumptions, making them challenging to simulate on a large scale. Alternatively, conventional deep learning-based approaches are pure data driven and hence, do not have physiological interpretation. The present disclosure provides a method that effectively combines both physiological domain knowledge and deep learning to enable simulation of realistic cardiovascular disease specific biomedical signals. An ensemble Generative Adversarial Network (GAN) including a Long Short-Term Memory GAN (LSTM-GAN) configured to generate a Heart Rate Variability (HRV) pattern associated with the cardiovascular disease condition and a Deep Convolutional GAN (DCGAN) configured to create a morphology of a representative cardiac cycle is provided. A complete waveform is simulated by combining an output from each GAN.

An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

A cardiac simulation method includes storing, in a memory, a measured electrophysiological (EP) map of at least part of wall tissue of a heart of a patient. Based on the stored EP map, simulated electrical activity in response to computer-simulated pacing, which simulates actual electrical activity that would occur across the wall tissue of the heart of the patient in response to actual pacing, is calculated in a processor. Based on the simulated electrical activity calculated in the processor, one or more candidate locations on the wall tissue of the heart at which arrhythmia is suspected of originating are identified and indicated to a user.