Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks

doi:10.1109/JBHI.2019.2942938

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	Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks
	Niu, Jinghao 1,2; Tang, Yongqiang 1,2; Sun, Zhengya 1,2; Zhang, Wensheng 1,2
刊名	IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
	2020-05-01
卷号	24 期号:5 页码:1321-1332
关键词	Heart beat Electrocardiography Heart rate variability Deep learning Feature extraction Task analysis Informatics ECG classification biomedical monitoring convolutional neural network deep learning
ISSN号	2168-2194
DOI	10.1109/JBHI.2019.2942938
通讯作者	Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要	This paper presents a novel deep learning framework for the inter-patient electrocardiogram (ECG) heartbeat classification. A symbolization approach especially designed for ECG is introduced, which can jointly represent the morphology and rhythm of the heartbeat and alleviate the influence of inter-patient variation through baseline correction. The symbolic representation of the heartbeat is used by a multi-perspective convolutional neural network (MPCNN) to learn features automatically and classify the heartbeat. We evaluate our method for the detection of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) on MIT-BIH arrhythmia dataset. Compared with the state-of-the-art methods based on manual features or deep learning models, our method shows superior performance: the overall accuracy of 96.4%, F1 scores for SVEB and VEB of 76.6% and 89.7%, respectively. The ablation study on our method validates the effectiveness of the proposed symbolization approach and joint representation architecture, which can help the deep learning model to learn more general features and improve the ability of generalization for unseen patients. Because our method achieves a competitive inter-patient heartbeat classification performance without complex handcrafted features or the intervention of the human expert, it can also be adjusted to handle various other tasks relative to ECG classification.
资助项目	National Key R&D Program of China[2017YFC0803700] ; National Natural Science Foundation of China[61432008] ; National Natural Science Foundation of China[61876183] ; Beijing Municipal Natural Science Foundation[4172063]
WOS关键词	HEARTBEAT CLASSIFICATION ; ARRHYTHMIA DETECTION ; FEATURES
WOS研究方向	Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000535614100009
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/39530]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Zhang, Wensheng
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Niu, Jinghao,Tang, Yongqiang,Sun, Zhengya,et al. Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2020,24(5):1321-1332.
APA	Niu, Jinghao,Tang, Yongqiang,Sun, Zhengya,&Zhang, Wensheng.(2020).Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,24(5),1321-1332.
MLA	Niu, Jinghao,et al."Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 24.5(2020):1321-1332.