Abstract: The systems and methods can accurately and efficiently determine abnormal cardiac activity from motion data and/or cardiac data using techniques that can be used for long-term monitoring of a patient. In some embodiments, the method for using machine learning to determine abnormal cardiac activity may include receiving one or more periods of time of cardiac data and motion data for a subject. The method may include applying a trained deep learning architecture to each tensor of the one or more periods of time to classify each window and/or each period into one or more classes using at least the one or more signal quality indices for the cardiac data and the motion data and cardiovascular features. The deep learning architecture may include a convolutional neural network, a bidirectional recurrent neural network, and an attention network. The one or more classes may include abnormal cardiac activity and normal cardiac activity.

Abstract: Systems, methods, and computer-readable media for classifying a PTSD status. In an embodiment, an example method for using a classifier can comprise receiving information from electrocardiography performed on an individual; determining features from the information; comparing the features to the a logistic regression classifier trained using features determined from median quiescent segments of RR interval information from individuals with and without PTSD, wherein the median quiescent segments are non-overlapping time periods of lowest median HR for each individual, and the features include one or more of the following: deceleration capacity (DC), low frequency (LF) power, very low frequency (VLF) power, and standard deviation of all normal RR intervals (SDNN); and determining a posttraumatic stress disorder (PTSD) status of the individual based on the comparison of the features to the classifier, wherein the PTSD status is a severity of PTSD based on a probability of PTSD.

Abstract: The systems and methods can accurately and efficiently determine abnormal cardiac activity from motion data and/or cardiac data using techniques that can be used for long-term monitoring of a patient. In some embodiments, the method for using machine learning to determine abnormal cardiac activity may include receiving one or more may include applying a trained deep learning architecture to each tensor of the one or more periods of time to classify each window and/or each period into one or more classes using at least the one or more signal quality indices for the cardiac data and the motion data and cardiovascular features. The deep learning architecture may include a convolutional neural network, a bidirectional recurrent neural network, and an attention network. The one or more classes may include abnormal cardiac activity and normal cardiac activity.

Abstract: Systems, methods, and computer-readable media for classifying a PTSD status. In an embodiment, an example method for using a classifier can comprise receiving information from electrocardiography performed on an individual; determining features from the information; comparing the features to the a logistic regression classifier trained using features determined from median quiescent segments of RR interval information from individuals with and without PTSD, wherein the median quiescent segments are non-overlapping time periods of lowest median HR for each individual, and the features include one or more of the following: deceleration capacity (DC), low frequency (LF) power, very low frequency (VLF) power, and standard deviation of all normal RR intervals (SDNN); and determining a posttraumatic stress disorder (PTSD) status of the individual based on the comparison of the features to the classifier, wherein the PTSD status is a severity of PTSD based on a probability of PTSD.