Compressed sensing framework of data reduction at multiscale level for eigenspace multichannel ECG signals

Multichannel elctrocardiogram (MECG) signals are correlated both in spatial domain as well as in temporal domain and this correlation becomes even higher at multiscale levels. This work presents a MECG compression method in order to exploit the inherent inter-channel correlation more efficiently, using a multiscale compressive sensing (MSCS) based approach. Principal component analysis (PCA) is used to decorrelate the subband signals from different channels at each wavelet scale and then the significant eigenspace signals from higher frequency subbands are undergone through multiscale compressed sensing (CS). Since CS is well known for its effective representation of high dimensional sparse signals in terms of few random projections, here it confines the noise dominated high frequency clinical information of MECG signals to few compressed measurements which readily reduces the data size at the encoder side. Eigenspace is taken as the sparsifying basis for high frequency subband ECG signals. The proposed encoding strategy is implemented using a uniform scalar quantizer and a entropy encoder. Sparse signal recovery is done using a greedy sparse recovery algorithm called orthogonal matching pursuit (OMP). Performance evaluation of the coder is mainly carried out in terms of compression ratio (CR), root mean square difference (PRD), and wavelet energy based diagnostic distortion (WEDD). Simulation results give the lowest PRD value, 4.72% and WEDD value 3.28% at CR=10.84, for lead aVF for CSE multi-lead measurement library database.