Abstract
A Wireless Body Area network (WBAN) is a special purpose of Wireless Sensor Networks (WSNs) to connect various Biomedical Wireless Sensors (BWSs) located inside and outside of the human body to collect and transmit vital signals. The collected biomedical data send out via Gate Way (GW) to external databases at the hospitals and medical centers for diagnostic and therapeutic purposes. The electrocardiogram (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)based on Dynamic Thresholding Approach (DTA) is used to provide a robust low sampling-rate approach for normal and abnormal ECG signals. Advanced WBANs 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 35% reduction of Percentage Root-mean-square Difference (PRD) and a good level of quality for Signal to Noise Ratio (SNR).
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