Inter-Patient ECG Classification With Symbolic Representations and Multi-Perspective Convolutional Neural Networks
文献类型：期刊论文
| 作者 | Niu, Jinghao1,2 ; Tang, Yongqiang1,2; Sun, Zhengya1,2; Zhang, Wensheng1,2
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| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
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| 出版日期 | 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. |
| WOS关键词 | HEARTBEAT CLASSIFICATION ; ARRHYTHMIA DETECTION ; FEATURES |
| 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研究方向 | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:000535614100009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation |
| 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 自动化研究所_精密感知与控制研究中心 |
| 通讯作者 | Zhang, Wensheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, 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. |
入库方式： OAI收割
来源：自动化研究所
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