An ECG Compressed Sensing Method of Low Power Body Area Network

Abstract

Aimed at low power problem in body area network, an ECG compressed sensing method of low power body area network based on the compressed sensing theory was proposed. Random binary matrices were used as the sensing matrix to measure ECG signals on the sensor nodes. After measured value is transmitted to remote monitoring center, ECG signal sparse representation under the discrete cosine transform and block sparse Bayesian learning reconstruction algorithm is used to reconstruct the ECG signals. The simulation results show that the 30% of overall signal can get reconstruction signal which’s SNR is more than 60dB, each numbers in each rank of sensing matrix can be controlled below 5, which reduces the power of sensor node sampling, calculation and transmission. The method has the advantages of low power, high accuracy of signal reconstruction and easy to hardware implementation.

DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3995