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Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs: 2021 Computing in Cardiology, CinC 2021

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Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs: 2021 Computing in Cardiology, CinC 2021. / Chen, L.; Jiang, Z.; Almeida, T.P. et al.
2021 Computing in Cardiology (CinC). IEEE, 2022. p. 1-4.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Chen, L, Jiang, Z, Almeida, TP, Schlindwein, FS, Shoker, JS, Ng, GA, Zhou, H & Li, X 2022, Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs: 2021 Computing in Cardiology, CinC 2021. in 2021 Computing in Cardiology (CinC). IEEE, pp. 1-4, 2021 Computing in Cardiology (CinC), Brno, Czech Republic, 13/09/21. https://doi.org/10.23919/CinC53138.2021.9662757

APA

Chen, L., Jiang, Z., Almeida, T. P., Schlindwein, F. S., Shoker, J. S., Ng, G. A., Zhou, H., & Li, X. (2022). Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs: 2021 Computing in Cardiology, CinC 2021. In 2021 Computing in Cardiology (CinC) (pp. 1-4). IEEE. https://doi.org/10.23919/CinC53138.2021.9662757

Vancouver

Chen L, Jiang Z, Almeida TP, Schlindwein FS, Shoker JS, Ng GA et al. Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs: 2021 Computing in Cardiology, CinC 2021. In 2021 Computing in Cardiology (CinC). IEEE. 2022. p. 1-4 Epub 2021 Sept 15. doi: 10.23919/CinC53138.2021.9662757

Author

Chen, L. ; Jiang, Z. ; Almeida, T.P. et al. / Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs : 2021 Computing in Cardiology, CinC 2021. 2021 Computing in Cardiology (CinC). IEEE, 2022. pp. 1-4

Bibtex

@inproceedings{a7fb5b255eae4209972c7ff544b06f04,
title = "Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs: 2021 Computing in Cardiology, CinC 2021",
abstract = "Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our entry was not officially ranked and scored on the test data of the Challenge, because our code was not successfully processed during the official phase and failed to run with errors. {\textcopyright} 2021 Creative Commons.",
keywords = "Cardiology, Convolutional neural networks, Data mining, Extraction, Feature extraction, Recurrent neural networks, Automated feature extraction, Automatic classification, Automatic Detection, Cardiac disorders, Convolutional neural network, Learning methods, Multi-led ECG, Spatial features, Spatio-temporal, Spatiotemporal feature, Electrocardiography",
author = "L. Chen and Z. Jiang and T.P. Almeida and F.S. Schlindwein and J.S. Shoker and G.A. Ng and H. Zhou and X. Li",
year = "2022",
month = jan,
day = "10",
doi = "10.23919/CinC53138.2021.9662757",
language = "English",
isbn = "9781665467216",
pages = "1--4",
booktitle = "2021 Computing in Cardiology (CinC)",
publisher = "IEEE",
note = "2021 Computing in Cardiology (CinC) ; Conference date: 13-09-2021 Through 15-09-2021",
url = "http://www.cinc2021.org/about-the-conference/",

}

RIS

TY - GEN

T1 - Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs

T2 - 2021 Computing in Cardiology (CinC)

AU - Chen, L.

AU - Jiang, Z.

AU - Almeida, T.P.

AU - Schlindwein, F.S.

AU - Shoker, J.S.

AU - Ng, G.A.

AU - Zhou, H.

AU - Li, X.

PY - 2022/1/10

Y1 - 2022/1/10

N2 - Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our entry was not officially ranked and scored on the test data of the Challenge, because our code was not successfully processed during the official phase and failed to run with errors. © 2021 Creative Commons.

AB - Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our entry was not officially ranked and scored on the test data of the Challenge, because our code was not successfully processed during the official phase and failed to run with errors. © 2021 Creative Commons.

KW - Cardiology

KW - Convolutional neural networks

KW - Data mining

KW - Extraction

KW - Feature extraction

KW - Recurrent neural networks

KW - Automated feature extraction

KW - Automatic classification

KW - Automatic Detection

KW - Cardiac disorders

KW - Convolutional neural network

KW - Learning methods

KW - Multi-led ECG

KW - Spatial features

KW - Spatio-temporal

KW - Spatiotemporal feature

KW - Electrocardiography

U2 - 10.23919/CinC53138.2021.9662757

DO - 10.23919/CinC53138.2021.9662757

M3 - Conference contribution/Paper

SN - 9781665467216

SP - 1

EP - 4

BT - 2021 Computing in Cardiology (CinC)

PB - IEEE

Y2 - 13 September 2021 through 15 September 2021

ER -