Adaptive compression ratio estimation for categorified sparsity in real-time ECG monitoring system

Real-time electrocardiogram (ECG) monitoring system has sprung up due to the considerable interest attracted to Wireless Body Area Networks (WBANs). Commonly, the ECG data is required to be compressed for higher energy efficiency and Compressive Sensing (CS) has been proved to be an effective way. However, for the real-time ECG monitoring, the length of data frame should be strictly limited for short data latency, which unavoidably causes variation of data sparsity and fluctuation of reconstruction quality. Furthermore, the compression ratio is well worth considering with corresponding energy cost in WBANs. To balance the reconstruction quality and compression ratio, this paper illustrates an adaptive compression ratio estimation technique of ECG signal and proposes a common model of relationship between compression ratio and sparsity. Correlated-sparsity compression ratio (CoCR) is defined to reflect the influence of sparsity on compression performance. Moreover, a two-dimensional clustering algorithm is designed to accelerate the operation speed and improve the precision of classification without prior knowledge. Finally, simulation results verify that the proposed method can guarantee the reconstruction quality stable and improve the compression performance by 18.85% compared with traditional CS-based methods.