Domain adaptation methods for ECG classification

The detection and classification of heart arrhythmias using Electrocardiogram signals (ECG) has been an active area of research in the literature. Usually, to assess the effectiveness of a proposed classification method, training and test data are extracted from the same ECG record. However, in real scenarios test data may come from different records. In this case, the classification results may be less accurate due to the statistical shift between these samples. In order to solve this issue, we investigate, in this paper, the capabilities of two domain adaption methods proposed recently in the literature of machine learning. The first is known as domain transfer SVM, whereas the second is the importance weighted kernel logistic regression method. To assess the effectiveness of both methods, the MIT-BIH arrhythmia database is used in the experiments.