A novel approach to ECG classification based upon two-layered HMMS in body sensor networks
文献类型：期刊论文
| 作者 | Liang W(梁炜) ; Zhang YL(张吟龙); Tan JD(谈金东); Li Y(李杨)
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| 刊名 | SENSORS
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| 出版日期 | 2014 |
| 卷号 | 14期号:4页码:5994-6011 |
| 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) |
| ISSN号 | 1424-8220 |
| 产权排序 | 1 |
| 英文摘要 | 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关键词 | PREMATURE VENTRICULAR CONTRACTIONS ; HIDDEN MARKOV-MODELS ; INTERVAL FEATURES ; NEURAL-NETWORK ; ELECTROCARDIOGRAM |
| WOS研究方向 | Chemistry ; Electrochemistry ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000336784600013 |
| 源URL | [http://ir.sia.cn/handle/173321/14735]  |
| 专题 | 沈阳自动化研究所_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.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, United States 2.University of Chinese Academy of Sciences, Beijing 100049, China 3.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 100016, China |
| 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,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,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 14.4(2014):5994-6011. |
入库方式： OAI收割
来源：沈阳自动化研究所
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