Abstract
Myocardial infarctions (MIs) pose a significant risk to human health. Accurate identification and characterization of MI's are essential for the effective medical treatment. Traditional methods such as the standard 12-lead ECG identify MIs with the electrocardiogram (ECG) recorded on the body surface, which consider little about anatomical details of the human body. These methods are limited in the ability to map back the actual electrical activities of the heart and further characterize MIs. Inverse ECG (iECG) methods were proposed to trace the distribution of electric potentials on the heart surface and characterize MIs. However, these methods do not account for the spatiotemporal behaviors of the potential distributions, because the electric potentials are distributed in the complex geometry and varying dynamically over time. In this paper, a novel iECG model with spatiotemporal regularization is developed to image and characterize MIs. We solve the iECG problem with the method of spatiotemporal regularization and reconstruct electric potentials on the heart surface. Furthermore, we group the estimated heart potentials into healthy and infarct clusters with a wavelet-clustering method. Experimental results show that the proposed method effectively solves the iECG problem and better characterizes MIs compared with existing methods.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 120-123 |
| Number of pages | 4 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538624050 |
| DOIs | |
| State | Published - Apr 6 2018 |
| Event | 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018 - Las Vegas, United States Duration: Mar 4 2018 → Mar 7 2018 |
Other
| Other | 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 3/4/18 → 3/7/18 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Biomedical Engineering
- Health Informatics
Cite this
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Inverse ECG modeling with spatiotemporal regularization for the characterization of myocardial infarctions. / Yao, Bing; Zhu, Rui; Yang, Hui.
2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 120-123.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Inverse ECG modeling with spatiotemporal regularization for the characterization of myocardial infarctions
AU - Yao, Bing
AU - Zhu, Rui
AU - Yang, Hui
PY - 2018/4/6
Y1 - 2018/4/6
N2 - Myocardial infarctions (MIs) pose a significant risk to human health. Accurate identification and characterization of MI's are essential for the effective medical treatment. Traditional methods such as the standard 12-lead ECG identify MIs with the electrocardiogram (ECG) recorded on the body surface, which consider little about anatomical details of the human body. These methods are limited in the ability to map back the actual electrical activities of the heart and further characterize MIs. Inverse ECG (iECG) methods were proposed to trace the distribution of electric potentials on the heart surface and characterize MIs. However, these methods do not account for the spatiotemporal behaviors of the potential distributions, because the electric potentials are distributed in the complex geometry and varying dynamically over time. In this paper, a novel iECG model with spatiotemporal regularization is developed to image and characterize MIs. We solve the iECG problem with the method of spatiotemporal regularization and reconstruct electric potentials on the heart surface. Furthermore, we group the estimated heart potentials into healthy and infarct clusters with a wavelet-clustering method. Experimental results show that the proposed method effectively solves the iECG problem and better characterizes MIs compared with existing methods.
AB - Myocardial infarctions (MIs) pose a significant risk to human health. Accurate identification and characterization of MI's are essential for the effective medical treatment. Traditional methods such as the standard 12-lead ECG identify MIs with the electrocardiogram (ECG) recorded on the body surface, which consider little about anatomical details of the human body. These methods are limited in the ability to map back the actual electrical activities of the heart and further characterize MIs. Inverse ECG (iECG) methods were proposed to trace the distribution of electric potentials on the heart surface and characterize MIs. However, these methods do not account for the spatiotemporal behaviors of the potential distributions, because the electric potentials are distributed in the complex geometry and varying dynamically over time. In this paper, a novel iECG model with spatiotemporal regularization is developed to image and characterize MIs. We solve the iECG problem with the method of spatiotemporal regularization and reconstruct electric potentials on the heart surface. Furthermore, we group the estimated heart potentials into healthy and infarct clusters with a wavelet-clustering method. Experimental results show that the proposed method effectively solves the iECG problem and better characterizes MIs compared with existing methods.
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U2 - 10.1109/BHI.2018.8333384
DO - 10.1109/BHI.2018.8333384
M3 - Conference contribution
VL - 2018-January
SP - 120
EP - 123
BT - 2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -