Coding ECG beats using multiscale compressed sensing based processing

Abstract

Compressed sensing recovers a sparse signal from a small set of linear, nonadaptive measurements. A sparse signal can be represented by compressed measurements with a reduced number of projections on a set of random vectors. In this paper, a multiscale compressed sensing based processing is investigated for an electrocardiogram signal which yields coded measurements. In case of an electrocardiogram (ECG) signal, the coded measurements are expected to retain the clinical information. To achieve this, compressed sensing based processing is applied at each wavelet scale and measurements are coded using Huffman coder. The measurements at each scale use random sensing matrix with independent identically distributed (i.i.d.) entries formed by sampling a Gaussian distribution. The proposed method is evaluated using pathological ECG signals from the CSE database, synthetic and normal ECGs. It helps preserve the pathological information and clinical components in compressed signal. The compressed signal quality is evaluated using standard distortion measures and mean opinion score (MOS). The MOS values for the signals range from 5% to 8.3% with a wavelet energy based diagnostic distortion (WEDD) value of 9.46% which falls under the excellent category.