Cross-Database Generalization of Deep Learning Models for Arrhythmia Classification

Elena Merdjanovska, Aleksandra Rashkovska

Abstract: Arrhythmias are a wide-spread group of heart abnormalities. In the area of computational methods for ECG analysis, much research has been done on automated arrhythmia detection. In order to be able to develop such methods, databases containing various arrhythmia examples are necessary. The most popular such database is the MIT-BIH Arrhythmia database. Various deep learning architectures have shown superior arrhythmia classification performance on the MIT-BIH Arrhythmia database. However, the applicability of deep learning models for arrhythmia classification beyond their study-specific database has not been explored so far. In this paper, we test the cross-database generalization capabilities of a convolutional neural network, shown to successfully detect arrhythmias on MIT-BIH Arrhythmia, on three other public databases that contain the same arrhythmia groups, namely INCART, MIT-BIH Supraventricular Arrhythmia, and European ST-T. Additionally, our focus is on realistic evaluation schemes, namely inter-patient, to evaluate how well-established models are able to classify arrhythmias on a much larger number of distinct people not present in the training of the neural network. The results have shown that the cross-database generalization performance decreases if the conditions under which the measurements have been performed (lead position) in the other databases are not the same as in the MIT-BIH Arrhythmia database.