Title: A new real-time resource-efficient algorithm for ECG denoising, feature extraction and classification-based wearable sensor network

Authors: Ali Fadel Marhoon; Ali Hussein Hamad

Addresses: Computer Science Department, University of Basrah, Basrah, Iraq ' Information and Communication Engineering Department, University of Baghdad, Baghdad, Iraq

Abstract: Long-term patient monitoring is an important issue especially for the elderly. This can be done using a wearable wireless sensor network. These sensors have limited resources in terms of computation, storage memory, size and mainly in power. In this work, a real-time resource-efficient algorithm has been implemented and tested practically such that not all the Ephy (ECG) data are transmitted to the server for later processing. The algorithm reads a sample window and processes it on the sensor node using an adaptive filter with a differentiator and then a fast and simple algorithm for feature extraction of the ECG signal to find P, Q, R, S and T waves. Finally, a classifier algorithm has been designed to distinguish between normal and abnormal ECG signals. The work has been implemented using Shimmer sensor nodes and uses the open source TinyOS 2.1.2 and Python 2.7.

Keywords: wearable sensor networks; wireless sensor networks; WSNs; electrocardiograms; ECG signals; ECG denoising; feature extraction; TinyOS; real time; adaptive filtering; classification; resource-efficient algorithms; patient monitoring; biomedical technology; elderly patients; e-healthcare; electronic healthcare; healthcare technology.

DOI: 10.1504/IJBET.2015.070032

International Journal of Biomedical Engineering and Technology, 2015 Vol.18 No.2, pp.103 - 114

Received: 03 Oct 2014
Accepted: 21 Dec 2014

Published online: 25 Jun 2015 *

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