Abstract: A method for detecting long QT syndrome in a subject comprises obtaining data corresponding to an electrocardiogram (ECG) signal of the subject, identifying a set of features in the data based on selected inflection points of the ECG signal, using the set of features to categorize segments of the ECG signal, and using the categorized segments of the ECG signal and the inflection points to classify the ECG signal as normal or as long QT syndrome. Long QT syndrome is detected when the subject's ECG signal is classified as long QT syndrome. The method may include determining whether the long QT syndrome is Type 1 or Type 2.

Abstract: A non-invasive method of assessing a coronary stenosis or other blockage in an artery or other vasculature is based on determination of a blood flow characteristic. In some embodiments, the blood flow characteristic is a fractional flow reserve determined using a statistical correlation of experimentally determined physiological factors and anatomical factors. In other embodiments, the blood flow characteristic is a blood flow rate determined using machine learning techniques. In further embodiments, the blood flow rate determined using machine learning techniques is a physiological factor used in determining the fractional flow reserve.

Abstract: A non-invasive computer-based method and system for assessing a coronary stenosis or other blockage in an artery or other vasculature includes creating a computational model of the vasculature of interest, modeling blood flow through the vasculature, and determining the mean residence time through a given coronary artery segment, which is a direct assessment of physiological changes on the flow of blood as a result of the stenosis. In some embodiments, blood is modeled as a multi-phase fluid.

Abstract: A method for detecting long QT syndrome in a subject comprises obtaining data corresponding to an electrocardiogram (ECG) signal of the subject, identifying a set of features in the data based on selected inflection points of the ECG signal, using the set of features to categorize segments of the ECG signal, and using the categorized segments of the ECG signal and the inflection points to classify the ECG signal as normal or as long QT syndrome. Long QT syndrome is detected when the subject's ECG signal is classified as long QT syndrome. The method may include determining whether the long QT syndrome is Type 1 or Type 2.