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Patient-specific ECG classification by deeper CNN from generic to dedicated
Li, Yazhao1; Pang, Yanwei1; Wang, Jian1; Li, Xuelong2
Department光学影像学习与分析中心
2018-11-07
Source PublicationNEUROCOMPUTING
ISSN0925-2312;1879-2545
Volume314Pages:336-346
Contribution Rank2
Abstract

This paper presents a new mechanism which is more effective for wearable devices to classify patient-specific electrocardiogram (ECG) heartbeats. In our method, a Generic Convolutional Neural Network (GCNN) is trained first using a large number of heartbeats without distinguishing patients. Based on the GCNN, fine-tuning technique is applied to modify the GCNN to a Tuned Dedicated CNN (TDCNN) for the corresponding individual. Notably, only the GCNN instead of common training data is required to be stored into wearable devices. Moreover, only fine-tuning with several seconds rather than dozens of minutes is needed before the TDCNN is used to monitor the long-term ECG signals in clinical. To accelerate the ECG classification, only the original ECG heartbeat is input to the CNN without other extended information from the neighbor heartbeats or FFT representation. A deeper CNN architecture with small-scale convolutional kernels is adopted to improve the speed and accuracy for classification. With deeper CNN, hierarchical features can be extracted to help improve the accuracy of ECG classification. The state-of-the-art performance on efficiency and accuracy for ECG classification over MIT-BIH dataset is achieved by the proposed method. The effectiveness and superiority for detecting ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) events are demonstrated. The proposed mechanism of fine-tuning the GCNN to TDCNN improves the efficiency for training patient-specific CNN classifier. Because of the computational efficiency of fine-tuning, ECG diagnosis and heart monitoring can be easily implemented with popular wearable devices in practice. (C) 2018 Elsevier B.V. All rights reserved.

KeywordEcg Classification Deep Convolutional Neural Networks (Cnn) Generic Cnn (gCnn) Tuned Dedicated Cnn (tdCnn) Heart Monitoring Wearable Devices
DOI10.1016/j.neucom.2018.06.068
Indexed BySCI ; EI
Language英语
WOS IDWOS:000443718400033
PublisherELSEVIER SCIENCE BV
EI Accession Number20182905572520
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30617
Collection光学影像学习与分析中心
Corresponding AuthorPang, Yanwei
Affiliation1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
2.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Li, Yazhao,Pang, Yanwei,Wang, Jian,et al. Patient-specific ECG classification by deeper CNN from generic to dedicated[J]. NEUROCOMPUTING,2018,314:336-346.
APA Li, Yazhao,Pang, Yanwei,Wang, Jian,&Li, Xuelong.(2018).Patient-specific ECG classification by deeper CNN from generic to dedicated.NEUROCOMPUTING,314,336-346.
MLA Li, Yazhao,et al."Patient-specific ECG classification by deeper CNN from generic to dedicated".NEUROCOMPUTING 314(2018):336-346.
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