Wireless ECG systems with New Sampling-rate Approach Based on Compressed Sensing Theory

Abstract

The main drawback of current ECG systems is the location-specific nature of the system due to the use of fixed/wired systems. The currently ECG systems are also restricted by size, patient’s mobility, power, and transmission capacity. Therefore, the currently ECG systems need to be further developed in order to achieve extended mobility and wireless monitoring of several patients at the same time. The wireless ECG systems provide vital information about the heart to physicians and doctors at anytime and anywhere by removing constraints of time and location of patients while increasing both the mobility and the quality of healthcare systems. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Block Sparse Bayesian Learning (BSBL) framework is used to provide a robust low sampling-rate approach for wireless ECG systems. 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 15% reduction of Percentage Root- mean-square Difference (PRD) for a selected recode of ECG signals. The simulation results also show that sampling-rate can be minimized to 35% of nyquist-rate.

Index Terms- Sampling-rate, Signal-to-noise ratio, Wireless ECG Systems, Compressed Sensing, Block Sparse Bayesian learning