ECG codebook model for Myocardial Infarction detection

Abstract

ECG is a kind of high dimensional dataset and the useful information of illness only exists in few heartbeats. To achieve a good classification performance, most existing approaches used features proposed by human experts, and there is no approach for automatic useful feature extraction. To solve that problem, we propose an ECG Codebook Model (ECGCM) which automatically builds a small number of codes to represent the high dimension ECG data. ECGCM not only greatly reduces the dimension of ECG, but also contains more meaningful semantic information for Myocardial Infarction detection. Our experiment results show that ECGCM achieves 2% and 20.5% improvement in sensitivity and specificity respectively in Myocardial Infarction detection.