Latent topic multi-instance learning approach for automated ECG classification

This paper presents a new latent topic multiple instance learning (LTMIL) for automated ECG classification. Due to the characteristics of multiple beats constituting an ECG and the high cost of having all the beats manually labeled, supervised machine learning techniques have achieved limited success in ECG classification. In this paper, we first discuss the rational for applying multiple instance learning (MIL) to automated ECG classification and propose a new MIL strategy called LTMIL for it which integrates the intra and inter ECG difference. It is a three hierarchical model. Firstly, we cluster all unlabeled beats into k topics using variable weighting, by which beats are reshaped to be dense and separable. An ECG and its beats are then represented as mixtures over topics. Consequently, the intra and inter ECG difference can be fully embodied in the difference between mixtures over topics. Finally, any supervised learning techniques can be applied to classification of transformed ECGs. Our experimental results on real ECG datasets from the PTB diagnostic database demonstrate that compared with existing multiple instance learning and supervised machine learning algorithms, the proposed algorithm is able to automatically classify ECG without labeling beats and improves the classification quality in terms of sensitivity and specificity.