A Spectrogram Based Novel Approach for Arrhythmia Detection with Convolutional Neural Networks

Abstract ECG is one of the most important medical scans which is used for diagnosis of various heart related conditions and diseases. One of the most common of these is arrhythmia, which is caused by the irregularity of the heart beats. Artificial Intelligence has had a major impact in the field of vital monitoring and autonomous medical diagnosis. Therefore, a lot of work has demonstrated its effectiveness in arrhythmia detection. In this paper, we propose a method that tries to improve upon the accuracy of such models with the help of a light weight deep learning architecture that utilized 2D Separable CNN with a group of graphical representations of the ECG signals like the STFT, CWT and MFCC. Our model has achieved an accuracy of 97.41 and an F1 score of 88.20 on a processed version of the MIT-BIH dataset and takes on an average 7.93 times less calculations compared to a simple 2D Convolution model.