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A novel approach to ECG classification based upon two-layered HMMS in body sensor networks
Liang W(梁炜); Zhang YL(张吟龙); Tan JD(谈金东); Li Y(李杨)
作者部门工业控制网络与系统研究室
关键词Electrocardiography (Ecg) Integral-coefficient-band-stop (Icbs) Filter Expert-annotation Assisted Baum-welch Algorithm Two-layered Hidden Markov Model Body Sensor Network (Bsn)
发表期刊Sensors (Switzerland)
ISSN1424-8220
2014
卷号14期号:4页码:5994-6011
收录类别SCI ; EI
EI收录号20141517555310
WOS记录号WOS:000336784600013
产权排序1
摘要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. © 2014 by the authors; licensee MDPI, Basel, Switzerland.
语种英语
WOS标题词Science & Technology ; Physical Sciences ; Technology
WOS类目Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
关键词[WOS]PREMATURE VENTRICULAR CONTRACTIONS ; HIDDEN MARKOV-MODELS ; INTERVAL FEATURES ; NEURAL-NETWORK ; ELECTROCARDIOGRAM
WOS研究方向Chemistry ; Electrochemistry ; Instruments & Instrumentation
引用统计
被引频次:13[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/14735
专题工业控制网络与系统研究室
通讯作者Liang W(梁炜)
作者单位1.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 100016, 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, United States
推荐引用方式
GB/T 7714
Liang W,Zhang YL,Tan JD,et al. A novel approach to ECG classification based upon two-layered HMMS in body sensor networks[J]. Sensors (Switzerland),2014,14(4):5994-6011.
APA Liang W,Zhang YL,Tan JD,&Li Y.(2014).A novel approach to ECG classification based upon two-layered HMMS in body sensor networks.Sensors (Switzerland),14(4),5994-6011.
MLA Liang W,et al."A novel approach to ECG classification based upon two-layered HMMS in body sensor networks".Sensors (Switzerland) 14.4(2014):5994-6011.
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