ECG-Based Heartbeat Classification Using Two-Level Convolutional Neural Network and RR Interval Difference

Yande XIANG; Jiahui LUO; Taotao ZHU; Sheng WANG; Xiaoyan XIANG; Jianyi MENG

doi:10.1587/transinf.2017EDP7285

ECG-Based Heartbeat Classification Using Two-Level Convolutional Neural Network and RR Interval Difference

Yande XIANG, Jiahui LUO, Taotao ZHU, Sheng WANG, Xiaoyan XIANG, Jianyi MENG

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Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.4 pp.1189-1198

Publication Date: 2018/04/01

Publicized: 2018/01/12

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDP7285

Type of Manuscript: PAPER

Category: Biological Engineering

Authors

Yande XIANG
  Zhejiang University
Jiahui LUO
  Zhejiang University
Taotao ZHU
  Zhejiang University
Sheng WANG
  Zhejiang University
Xiaoyan XIANG
  Fudan University
Jianyi MENG
  Fudan University

Keyword

electrocardiogram (ECG), beat classification, convolutional neural network (CNN), biomedical signal processing, multi-layer perceptron (MLP)

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Yande XIANG, Jiahui LUO, Taotao ZHU, Sheng WANG, Xiaoyan XIANG, Jianyi MENG, "ECG-Based Heartbeat Classification Using Two-Level Convolutional Neural Network and RR Interval Difference" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 4, pp. 1189-1198, April 2018, doi: 10.1587/transinf.2017EDP7285.
Abstract: Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7285/_p

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@ARTICLE{e101-d_4_1189,
author={Yande XIANG, Jiahui LUO, Taotao ZHU, Sheng WANG, Xiaoyan XIANG, Jianyi MENG, },
journal={IEICE TRANSACTIONS on Information},
title={ECG-Based Heartbeat Classification Using Two-Level Convolutional Neural Network and RR Interval Difference},
year={2018},
volume={E101-D},
number={4},
pages={1189-1198},
abstract={Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.},
keywords={},
doi={10.1587/transinf.2017EDP7285},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - ECG-Based Heartbeat Classification Using Two-Level Convolutional Neural Network and RR Interval Difference
T2 - IEICE TRANSACTIONS on Information
SP - 1189
EP - 1198
AU - Yande XIANG
AU - Jiahui LUO
AU - Taotao ZHU
AU - Sheng WANG
AU - Xiaoyan XIANG
AU - Jianyi MENG
PY - 2018
DO - 10.1587/transinf.2017EDP7285
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2018
AB - Arrhythmia classification based on electrocardiogram (ECG) is crucial in automatic cardiovascular disease diagnosis. The classification methods used in the current practice largely depend on hand-crafted manual features. However, extracting hand-crafted manual features may introduce significant computational complexity, especially in the transform domains. In this study, an accurate method for patient-specific ECG beat classification is proposed, which adopts morphological features and timing information. As to the morphological features of heartbeat, an attention-based two-level 1-D CNN is incorporated in the proposed method to extract different grained features automatically by focusing on various parts of a heartbeat. As to the timing information, the difference between previous and post RR intervels is computed as a dynamic feature. Both the extracted morphological features and the interval difference are used by multi-layer perceptron (MLP) for classifing ECG signals. In addition, to reduce memory storage of ECG data and denoise to some extent, an adaptive heartbeat normalization technique is adopted which includes amplitude unification, resolution modification, and signal difference. Based on the MIT-BIH arrhythmia database, the proposed classification method achieved sensitivity Sen=93.4% and positive predictivity Ppr=94.9% in ventricular ectopic beat (VEB) detection, sensitivity Sen=86.3% and positive predictivity Ppr=80.0% in supraventricular ectopic beat (SVEB) detection, and overall accuracy OA=97.8% under 6-bit ECG signal resolution. Compared with the state-of-the-art automatic ECG classification methods, these results show that the proposed method acquires comparable accuracy of heartbeat classification though ECG signals are represented by lower resolution.
ER -