Abstract: A method comprising: at a training stage, training a machine learning algorithm on a training set comprising: (i) Heart Rate Variability (HRV) parameters extracted from temporal beat activity samples, wherein at least some of said samples include a representation of a Ventricular Fibrillation (VF) event, (ii) labels associated with one of: a first period of time immediately preceding a VF event in a temporal beat activity sample, a second period of time immediately preceding the first period of time in a temporal beat activity sample, and all other periods of time in a temporal beat activity sample; at an inference stage, receiving, as input, a target HRV parameters representing temporal beat activity in a subject; and applying said machine learning algorithm to said target HRV parameters, to predict an onset time of a VF event in said subject.

Abstract: A method comprising: receiving an image stream of a mammalian cardiac muscle cell; defining or having defined a region of interest (ROI) within said cell; calculating a fast Fourier transform (FFT) of said ROI; detecting peaks in said FFT; and determining at least one of sarcomere length, mitochondrial length, and mitochondrial width, based, at least in part, in said detected peaks.

Abstract: A method comprising: receiving a plurality of data segments each representing human cardiac activity; calculating, from each of said data segments, a first value representing a distribution of a heart rate variability (HRV) parameter; acquiring a signal representing cardiac activity in a patient; deriving a second value representing distribution of an HRV parameter in said signal; and indicating an atrial fibrillation (AF) event in said patient, when a difference between said first and second values is above a predetermined threshold.

Abstract: A method for automatic determining of a state of a heart of a patient based on multiple-lead ECG information of the patient, the method includes (a) receiving the multiple-lead ECG information of the patient, by a computerized system; and (b) applying one or more machine learning processes on the multiple-lead ECG information of the patient to determine the state of a heart of the patient, wherein the state of the heart comprises at least one heart condition of multiple types of heart conditions. One, some or all of the one or more machine learning processes was trained using a dataset that comprises multiple computer generated images, the multiple computer generated images represent images acquired by an image acquisition process of ECG plots, wherein the ECG plots are generated based on digital multiple-lead ECG signals.

Abstract: A method for evaluating heart function, comprising receiving a digitized cardiac signal, computing a power spectral density (PSD) or multiscale entropy (MSE) of the digitized cardiac signal, and evaluating heart function based on the PSD or MSE is provided. Systems and computer program products for doing same are also provided.

Abstract: A method comprising: at a training stage, training a machine learning algorithm on a training set comprising: (i) Heart Rate Variability (HRV) parameters extracted from temporal beat activity samples, wherein at least some of said samples include a representation of a Ventricular Fibrillation (VF) event, (ii) labels associated with one of: a first period of time immediately preceding a VF event in a temporal beat activity sample, a second period of time immediately preceding the first period of time in a temporal beat activity sample, and all other periods of time in a temporal beat activity sample; at an inference stage, receiving, as input, a target HRV parameters representing temporal beat activity in a subject; and applying said machine learning algorithm to said target HRV parameters, to predict an onset time of a VF event in said subject.

Abstract: A method comprising: receiving a plurality of data segments each representing human cardiac activity; calculating, from each of said data segments, a first value representing a distribution of a heart rate variability (HRV) parameter; acquiring a signal representing cardiac activity in a patient; deriving a second value representing distribution of an HRV parameter in said signal; and indicating an atrial fibrillation (AF) event in said patient, when a difference between said first and second values is above a predetermined threshold.

Abstract: A system comprising at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, a signal representing temporal beat activity in a mammalian, receive indication of user selection of an integration level of said beat rate activity, receive indication of user selection of a species of said mammalian, and determine heart rate variation (HRV) in said signal, based, at least in part, on said type of said beat rate activity and said species of said mammalian.

Abstract: A method comprising: receiving an image stream of a mammalian cardiac muscle cell; defining or having defined a region of interest (ROI) within said cell; calculating a fast Fourier transform (FFT) of said ROI; detecting peaks in said FFT; and determining at least one of sarcomere length, mitochondrial length, and mitochondrial width, based, at least in part, in said detected peaks

Abstract: A method for evaluating heart function, comprising receiving a digitized cardiac signal, computing a power spectral density (PSD) or multiscale entropy (MSE) of the digitized cardiac signal, and evaluating heart function based on the PSD or MSE is provided. Systems and computer program products for doing same are also provided.