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

doi:10.1016/j.artmed.2019.101788

	An intelligent learning approach for improving ECG signal classification and arrhythmia analysis
	Sangaiah, Arun Kumar 1,2; Arumugam, Maheswari 1; Bian, Gui-Bin2,3
发表期刊	ARTIFICIAL INTELLIGENCE IN MEDICINE
ISSN	0933-3657
	2020-03-01
卷号	103 页码:14
通讯作者	Bian, Gui-Bin(guibin.bian@ia.ac.cn)
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.
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
DOI	10.1016/j.artmed.2019.101788
关键词[WOS]	POWER-LINE INTERFERENCE ; FEATURE-EXTRACTION ; REMOVAL ; FILTER
收录类别	SCI
语种	英语
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]
项目资助者	Chinese Academy of Sciences (CAS) President's International Fellowship Initiative (PIFI) ; Youth Innovation Promotion Association of the Chinese Academy of Sciences
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Biomedical ; Medical Informatics
WOS记录号	WOS:000521117900024
出版者	ELSEVIER
七大方向——子方向分类	多模态智能
引用统计	被引频次：79[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38792
专题	复杂系统认知与决策实验室_先进机器人
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
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.