Disease-aware Compression of Multi-lead Electrocardiogram Using Intelligent Hybrid Encoder

Abstract

In this paper, we describe a multi-lead electrocardiogram (MECG) compression technique, which preserves pathological information in different affected leads while achieving high overall compression. For non-affected leads, the principal component decomposed expansion coefficients were optimally quantized using a feed-forward neural network. For affected leads, the wavelet decomposed coefficients were quantized using a fixed level. The proposed technique was evaluated with 130 ECG records with three major classes of myocardial infarction under Physionet. An average overall compression ratio of 21.25, with low values of percentage root mean squared difference of 2.45 for the affected lead group, was obtained.