IEICE TRANSACTIONS on Information
Patient-Specific ECG Classification with Integrated Long Short-Term Memory and Convolutional Neural Networks
Summary :
This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.5 pp.1153-1163
- Publication Date
- 2020/05/01
- Publicized
- 2020/02/13
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7282
- Type of Manuscript
- PAPER
- Category
- Biological Engineering
Authors
Jiaquan WU
Zhejiang University
Feiteng LI
Zhejiang University
Zhijian CHEN
Zhejiang University
Xiaoyan XIANG
Fudan University
Yu PU
Alibaba DAMO computing research lab
Keyword
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Jiaquan WU, Feiteng LI, Zhijian CHEN, Xiaoyan XIANG, Yu PU, "Patient-Specific ECG Classification with Integrated Long Short-Term Memory and Convolutional Neural Networks" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 1153-1163, May 2020, doi: 10.1587/transinf.2019EDP7282.
Abstract: This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7282/_p
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@ARTICLE{e103-d_5_1153,
author={Jiaquan WU, Feiteng LI, Zhijian CHEN, Xiaoyan XIANG, Yu PU, },
journal={IEICE TRANSACTIONS on Information},
title={Patient-Specific ECG Classification with Integrated Long Short-Term Memory and Convolutional Neural Networks},
year={2020},
volume={E103-D},
number={5},
pages={1153-1163},
abstract={This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.},
keywords={},
doi={10.1587/transinf.2019EDP7282},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Patient-Specific ECG Classification with Integrated Long Short-Term Memory and Convolutional Neural Networks
T2 - IEICE TRANSACTIONS on Information
SP - 1153
EP - 1163
AU - Jiaquan WU
AU - Feiteng LI
AU - Zhijian CHEN
AU - Xiaoyan XIANG
AU - Yu PU
PY - 2020
DO - 10.1587/transinf.2019EDP7282
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.
ER -
English
