New sampling approach for wireless ECG systems with compressed sensing theory

Wireless Body Area Networks (WBANs) consist of small intelligent biomedical wireless sensors attached on or implanted to the body to collect vital biomedical data such as electrocardiogram (ECG) signals to provide continuous health monitoring systems for diagnostic and therapeutic purposes. ECG signals are widely used in health care systems because they are noninvasive mechanisms to establish medical diagnosis of heart diseases. In order to fully exploit the benefits of WBANs to Electronic Health (EH), Mobile Health (MH), and Ambulatory Health Monitoring Systems (AHMS) the power consumption and sampling rate should be restricted to a minimum. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Block Sparse Bayesian Learning (BSBL) framework is used to provide new sampling approach for wireless ECG systems with CS theory. Advanced wireless ECG systems based on our approach will be able to deliver healthcare not only to patients in hospital and medical centers; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results illustrate 25% reduction of Percentage Root-mean-square Difference (PRD) and a good level of quality for Signal to Noise Ratio (SNR), sampling-rate, and power consumption.