Abstract: Methods, systems and media for computer vision using 2D convolution of 4D video data tensors are described. 3D convolution operations performed on 5D input tensors are simulated by performing 2D convolution of 4D tensors instead. A convolution block of a CNN performs two parallel operations: a spatial processing branch performs spatial feature extraction on a 4D tensor using 2D convolution, whereas a temporal processing branch performs temporal feature extraction on a different 4D tensor using 2D convolution. The output tensors of the spatial processing branch and the temporal processing branch are combined to generate an output tensor of the convolution block. The convolution block may include additional operations such as reshaping and/or further convolution operations to generate identically-sized output tensors for each branch, thereby eliminating the need for post-processing of the branches' output tensors prior to combining them.

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 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.