Multi-channel ECG data compression using compressed sensing in eigenspace

In recent years, compressed sensing (CS) has emerged as a potential alternative to traditional data compression techniques for resource-constrained telemonitoring applications. In the present work, a CS framework of data reduction is proposed for multi-channel electrocardiogram (MECG) signals in eigenspace. The sparsity of dimension-reduced eigenspace MECG signals is exploited to apply CS. First, principal component analysis (PCA) is applied over the MECG data to retain diagnostically important ECG features in a few principal eigenspace signals based on maximum variance. Then, the significant eigenspace signals are randomly projected over a sparse binary sensing matrix to obtain the reduced dimension compressive measurement vectors. The compressed measurements are quantized using a uniform quantizer and encoded by a lossless Huffman encoder. The signal recovery is carried out by an orthogonal matching pursuit (OMP) algorithm. The proposed method is evaluated on the MECG signals from PTB and CSE multilead measurement library databases. The average value of percentage root mean square difference (PRD) across the PTB database is found to be 5.24% at a compression ratio (CR)=17.76 in Lead V3 of PTB database. The visual signal quality of the reconstructed MECG signals is validated through mean opinion score (MOS), found to be 6.66%, which implies very good quality signal reconstruction.