Final published version
Licence: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -