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Sensors 2014, 14(4), 5994-6011; doi:10.3390/s140405994
Article

A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

1,* , 1,2
, 3
 and 1,2
1 Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA
* Author to whom correspondence should be addressed.
Received: 18 December 2013 / Revised: 24 February 2014 / Accepted: 18 March 2014 / Published: 27 March 2014
(This article belongs to the Section Physical Sensors)

Abstract

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient’s ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen.
Keywords: electrocardiography (ECG); integral-coefficient-band-stop (ICBS) filter; expert-annotation assisted Baum-Welch algorithm; two-layered hidden Markov model; body sensor network (BSN) electrocardiography (ECG); integral-coefficient-band-stop (ICBS) filter; expert-annotation assisted Baum-Welch algorithm; two-layered hidden Markov model; body sensor network (BSN)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Liang, W.; Zhang, Y.; Tan, J.; Li, Y. A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks. Sensors 2014, 14, 5994-6011.

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