Author : Arundhati B 1
Date of Publication :14th June 2017
Abstract: Remote Health Monitoring is emerging as the smart healthcare solution due to the technological breakthroughs in bio-medical field. One such promising health care application is ECG monitoring to detect cardiac diseases. This is made possible through Wireless Body Area Network (WBAN) which consists of wearable intelligent sensor nodes on human body. These nodes are responsible for acquiring and sending the signals to healthcare centres. Huge data is difficult to store as well as to transmit over energy constrained sensor nodes. Energy-efficient compression techniques offer promising solutions to overcome these drawbacks. The algorithms uses joint compression of Multi-channel ECG (MECG) signals through compressive sensing and joint reconstruction by solving convex optimization problem through Mixed Norm Minimization (MNM). Two channel ECG signals are collected from MIT-BIH Arrhythmia database record 100. Discrete Wavelet Transform is applied for both channels to make signals sparse. Sparse Signals are jointly compressed using sensing matrix and are jointly reconstructed using MNM. Matlab simulation shows good reconstruction quality of 2- channelECG signals with PRD of 0.60 and 0.53 for channel 1 and channel 2 respectively.
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