Robust Ultra-Low-Power Algorithm for Normal and Abnormal ECG Signals Based on Compressed Sensing Theory

The main drawback of current ECG systems is the location-specific nature of the systems due to the use of

fixed/wired applications. That is why there is a critical need to improve the current ECG systems to achieve extended patient's mobility and to cover security handling. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Sensing Matrix Selection (SMS) approach are used to provide a robust ultra-low-power approach for normal and abnormal ECG signals. Our simulation results based on two proposed algorithms illustrate 15% increase in Signal to Noise Ratio (SNR) and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices. The simulation results also confirm that the Binary Toeplitz Matrix (BTM) provides the best SNR and compression performance with the highest energy efficiency for random sensing matrix.