Detecting ECG abnormalities via transductive transfer learning

Detecting Electrocardiogram (ECG) abnormalities is the process of identifying irregular cardiac activities which may lead to severe heart damage or even sudden death. Due to the rapid development of cyberphysic systems and health informatics, embedding the function of ECG abnormality detection to various devices for real time monitoring has attracted more and more interest in the past few years. The existing machine learning and pattern recognition techniques developed for this purpose usually require sufficient labeled training data for each user. However, obtaining such supervised information is difficult, which makes the proposed ECG monitoring function unrealistic. To tackle the problem, we take advantage of existing well labeled ECG signals and propose a transductive transfer learning framework for the detection of abnormalities in ECG. In our model, unsupervised signals from target users are classified with knowledge transferred from the supervised source signals. In the experimental evaluation, we implemented our method on the MIT-BIH Arrhythmias Dataset and compared it with both anomaly detection and transductive learning baseline approaches. Extensive experiments show that our proposed algorithm remarkably outperforms all the compared methods, proving the effectiveness of it in detecting ECG abnormalities.