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An intelligent learning approach for improving ECG signal classification and arrhythmia analysis

文献类型：期刊论文

作者Sangaiah, Arun Kumar1,2; Arumugam, Maheswari1; Bian, Gui-Bin2,3
刊名ARTIFICIAL INTELLIGENCE IN MEDICINE
出版日期2020-03-01
卷号103页码:14
关键词ECG Noise suppression Baseline wander (BW) Power line interference (PLI) Electromyography (EMG) Signal to noise ratio (SNR) Devoted wavelet Feature extraction HMM (Hidden Markov Model) Cardiac arrhythmia
ISSN号0933-3657
DOI10.1016/j.artmed.2019.101788
通讯作者Bian, Gui-Bin(guibin.bian@ia.ac.cn)
英文摘要The recognition of cardiac arrhythmia in minimal time is important to prevent sudden and untimely deaths. The proposed work includes a complete framework for analyzing the Electrocardiogram (ECG) signal. The three phases of analysis include 1) the ECG signal quality enhancement through noise suppression by a dedicated filter combination; 2) the feature extraction by a devoted wavelet design and 3) a proposed hidden Markov model (HMM) for cardiac arrhythmia classification into Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Premature Ventricular Contraction (PVC) and Atrial Premature Contraction (APC). The main features extracted in the proposed work are minimum, maximum, mean, standard deviation, and median. The experiments were conducted on forty-five ECG records in MIT BIH arrhythmia database and in MIT BIN noise stress test database. The proposed model has an overall accuracy of 99.7 % with a sensitivity of 99.7 % and a positive predictive value of 100 %. The detection error rate for the proposed model is 0.0004. This paper also includes a study of the cardiac arrhythmia recognition using an IoMT (Internet of Medical Things) approach.
WOS关键词POWER-LINE INTERFERENCE ; FEATURE-EXTRACTION ; REMOVAL ; FILTER
资助项目Chinese Academy of Sciences (CAS) President's International Fellowship Initiative (PIFI)[2019VTB0005] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences[2018165]
WOS研究方向Computer Science ; Engineering ; Medical Informatics
语种英语
WOS记录号WOS:000521117900024
出版者ELSEVIER
资助机构Chinese Academy of Sciences (CAS) President's International Fellowship Initiative (PIFI) ; Youth Innovation Promotion Association of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/38791]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Bian, Gui-Bin
作者单位1.Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, Tamil Nadu, India
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China
推荐引用方式
GB/T 7714		 Sangaiah, Arun Kumar,Arumugam, Maheswari,Bian, Gui-Bin. An intelligent learning approach for improving ECG signal classification and arrhythmia analysis[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE,2020,103:14.
APA Sangaiah, Arun Kumar,Arumugam, Maheswari,&Bian, Gui-Bin.(2020).An intelligent learning approach for improving ECG signal classification and arrhythmia analysis.ARTIFICIAL INTELLIGENCE IN MEDICINE,103,14.
MLA Sangaiah, Arun Kumar,et al."An intelligent learning approach for improving ECG signal classification and arrhythmia analysis".ARTIFICIAL INTELLIGENCE IN MEDICINE 103(2020):14.

入库方式： OAI收割

来源：自动化研究所

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