Low sampling rate algorithm for wireless ECG systems based on compressed sensing theory

Wireless body area networks (WBANs) consist of small intelligent biomedical wireless sensors attached on or implanted in the body to collect vital biomedical data from the human body providing continuous health monitoring systems. The WBANs promise to be a key element in wireless electrocardiogram (ECG) monitoring systems for next generation. ECG signals are widely used in healthcare systems as a noninvasive technique for diagnosis of heart conditions. However, the use of conventional ECG system is restricted by patient’s mobility, transmission capacity, and physical size. Therefore, there is a great demand to improve wireless ECG systems. With this in mind, compressed sensing (CS) procedure as a new sampling approach and the collaboration of the sensing matrix selection algorithm based on dynamic thresholding approach are used to provide a robust low-complexity detection algorithm in gateways and access points with high probability and enough accuracy. Advanced wireless ECG systems based on our approach will be able to deliver healthcare not only to patients in hospitals and medical centers, but also at their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results show an increment of 1 % for sensitivity as well as 0.15 % for the prediction level and good detection accuracy. The simulation results also confirm that the binary Toeplitz matrix provides the best signal-to-noise ratio and compression performance with the highest energy efficiency for random sensing matrix in CS procedure.