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题名: A novel approach to ECG classification based upon two-layered HMMS in body sensor networks
作者: 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)
刊名: Sensors (Switzerland)
ISSN号: 1424-8220
出版日期: 2014
卷号: 14, 期号:4, 页码:5994-6011
收录类别: SCI ; EI
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.
WOS记录号: WOS:000336784600013
WOS标题词: Science & Technology ; Physical Sciences ; Technology
类目[WOS]: Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
关键词[WOS]: PREMATURE VENTRICULAR CONTRACTIONS ; HIDDEN MARKOV-MODELS ; INTERVAL FEATURES ; NEURAL-NETWORK ; ELECTROCARDIOGRAM
研究领域[WOS]: Chemistry ; Electrochemistry ; Instruments & Instrumentation
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/14735
Appears in Collections:工业控制网络与系统研究室_期刊论文

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