A Brief Review: Compressed Sensing of ECG Signal For Wireless System

CS, as a new compression paradigm, relies on three main requirements: sparsity representation, incoherence measurement, and nonlinear reconstruction, which pertain to the signals of interest, the encoding modality, and the decoding method, respectively. The main goal of the CS is to accurately reconstruct a high dimensional sparse vector using a small number of linear measurements. As in wireless sensor networks they have used battery back-up for transmission of data to base stations and considerable energy has been lost during transmission of data packets. As in intra-hospital environment there is need of automated data collection system from different ECG acquisition nodes wirelessly handled by nurses/doctors, they send data to a single hub for further processing and by this they provide easiness of handling of ECG equipment‟s as there are no wires in this communications process. They send the acquired ECG signal by Zigbee etc. to nearest point. As ECG signals have repetitive pattern, they can be compressed and transmitted at the transmitter end and hence can save energy of the battery back-up of transmitters, there is need to explore compressed sensing for ECG signals. In this work we have studied the existed methods especially the better from all called DWT based compressed sensing techniques for ECG signals. Our aim is to propose further enhancements in the existed system. We have taken as objective for DWT and DCT combination which can be applied for getting better performance. In this work, we have reviewed some existed methods along with explanation of compressed sensing based on sparse matrices.