1-D Convolutional Neural Network for ECG Arrhythmia Classification

Automated electrocardiogram analysis and classification is nowadays a fundamental tool for monitoring patient heart activity and, consequently, his state of health. Indeed, the main interest is detecting the arise of cardiac pathologies such as arrhythmia. This paper presents a novel approach for automatic arrhythmia classification based on a 1D convolutional neural network. The input is given by the combination of several databases from Physionet and is composed of two leads, LEAD1 and LEAD2. Data are not preprocessed, and no feature extraction has been performed, except for the medical evaluation in order to label it. Several 1D network configurations are tested and compared in order to determine the best one w.r.t. heart-beat classification. The test accuracy of the proposed neural approach is very high (up to 95%). However, the goal of this work is also the interpretation not only of the results, but also of the behavior of the neural network, by means of confusion matrix analysis w.r.t. the different arrhythmia classes.