Model Based Compressed Sensing Reconstruction Algorithms for ECG Telemonitoring in WBANs

Wireless Body area networks (WBANs) consist of sensors that continuously monitor and transmit real time vital signals to a nearby coordinator and then to a remote terminal via the Internet. One of the most important signals for monitoring in WBANs is the electrocardiography (ECG) signal. The design of an accurate and energy efficient ECG telemonitoring system can be achieved by: i) reducing the amount of data that should be transmitted ii) minimizing the computational operations executed at any transmitter/receiver in a WBAN. To this end, compressed sensing (CS) approaches can offer a viable solution. In this paper, we propose two novel CS based ECG reconstruction algorithms that minimize the samples that are required to be transmitted for an accurate reconstruction, by exploiting the block structure of the ECG in the time domain (TD) and in an uncorrelated domain (UD). The proposed schemes require the solutions of second-order cone programming (SOCP) problems that are usually tackled by computational demanding interior point (IP) methods. To solve these problems efficiently, we develop a path-wise coordinate descent based scheme. The reconstruction accuracy is evaluated by the percentage root-mean-square difference (PRD) metric. A reconstructed signal is acceptable if and only if PRD<9%PRD<9%. Simulation studies carried out with real electrocardiographic (ECG) data, show that the proposed schemes, operating in both the TD and in the UD as compared to the conventional CS techniques, reduce the Compression Ratio (CR) by 20%20% and 44%44% respectively, offering at the same time significantly low computational complexity

A blind equalization algorithm for biological signals transmission

Direct transmission of biological signals such as electrocardiogram (ECG) and electroencephalogram (EEG) through mobile network provides practically unlimited movement of the patients and unlimited coverage area. However, transmission of such signals over a bandlimited channel or through a multipath propagation is subject to inter symbol interference (ISI), whereby adjacent symbols on the output of the channel smear and overlap each other causing degradation of error performance. Mitigation of such kind of distortion can be achieved through equalization filter. Recently an adaptive blind channel equalization using sinusoidally-distributed dithered signed-error constant modulus algorithm (DSE-CMA) has been proposed. In this paper we investigate the performance and the feasibility of this scheme for wireless ECG and EEG transmission. Also, this paper discusses the importance of adaptive blind equalizer for biological signals transmission over existing wireless networks such as Global System for Mobile Communications (GSM) and the Enhanced Data rates for GSM Evolution (EDGE). The geometrical-based hyperbolically distributed scatterers (GBHDS) channel model for macrocell environments was simulated with angular spreads (AS) taken from measurement data. Simulation results show that the low complexity of implementation and the fast convergence rate are the major advantages of deploying this scheme for telemedicine applications. It is also shown that the equalizer output signal is highly correlated with the original transmitted signal in time and joint time-frequency domains