Dictionary Learning-Based Multichannel ECG Reconstruction Using Compressive Sensing

Multi-channel electrocardiogram (MECG) compression on lightweight wireless body area network (WBAN) is highly challenging for long-term eHealthcare monitoring. Energy consumption involved in wireless transmission poses an obstacle in the implementation of WBAN. MECG is widely used to diagnose cardiovascular diseases (CVDs), which require a considerable time to extract sufficient clinically relevant data from subjects. Since wireless MECG data transmission dominates energy cost and memory consumption of wireless sensor nodes, it is favorable to reduce the data size without any loss of diagnostic information. Compressed sensing (CS) provides efficient encoding schemes for data reduction and energy consumption in wireless transmission. We propose an adaptive multiple dictionary learning-based joint CS model for MECG compression, which exploits spatial correlation and adaptive features existing in MECG signals. We demonstrate performance of the proposed algorithm with the Physikalisch-Technische Bundesanstalt database (PTB) for MECG compression. Results from the proposed scheme can achieve excellent reconstruction quality with fewer measurements than other existing CS-based approaches. It is the most suitable technique for long-term MECG compression using lightweight WBAN devices.