ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection

This paper presents a useful technique for totally automatic detection of myocardial infarction from patients' ECGs. Due to the large number of heartbeats constituting an ECG and the high cost of having all the heartbeats manually labeled, supervised learning techniques have achieved limited success in ECG classification. In this paper, we first discuss the rationale for applying multiple instance learning (MIL) to automated ECG classification and then propose a new MIL strategy called latent topic MIL, by which ECGs are mapped into a topic space defined by a number of topics identified over all the unlabeled training heartbeats and support vector machine is directly applied to the ECG-level topic vectors. Our experimental results on real ECG datasets from the PTB diagnostic database demonstrate that, compared with existing MIL and supervised learning algorithms, the proposed algorithm is able to automatically detect ECGs with myocardial ischemia without labeling any heartbeats. Moreover, it improves classification quality in terms of both sensitivity and specificity.