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.