JSM-2 Based Joint ECG Compression Exploiting Temporal and Structural Dependency
In Wireless Body Area Networks (WBAN), the electrocardiogram (ECG) signal is an important class of bio-signals which needs to be transmitted and stored for diseases diagnostics. Due to the resource limitation in WBAN, the large amount of ECG signals need to be compressed before transmission and reconstructed with high accuracy. In this paper, we propose a novel CS-based ECG compression scheme, which considers both structural dependency and temporal dependency among ECG signals. The received ECG heartbeats are first classified into different classes and the statistical support information (SSI) is then established for each class. By using the corresponding SSI, a more accurate partially known support (PKS) will be obtained and the joint reconstruction performance of ECG signals could be improved consequently. Simulation results show that the proposed ECG compression scheme outperforms existing schemes, especially when the dimension of sampled measurements is low.