Compressed sensing of multi-lead ECG signals by compressive multiplexing

Compressed Sensing has recently been proposed for efficient data compression of multi-lead electrocardiogram recordings within ambulatory patient monitoring applications, e.g. wireless body sensor networks. However, current approaches only focus on signal reconstruction and do not consider the efficient compression of signal ensembles. In this work, we propose the utilization of a compressive multiplexing architecture that facilitates an efficient implementation of hardware compressed sensing for multi-lead ECG signals. For the reconstruction of ECG signal ensembles, we employ an greedy algorithm that exploits their joint sparsity structure. Our simulative study shows promising results which motivate further research in the field of compressive multiplexing for the acquisition multi-lead ECG signals.