- Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition
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- 论文类型： 期刊论文
- 发表时间： 2020-02-01
- 发表刊物： COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- 收录刊物： PubMed、SCIE、EI
- 卷号： 184
- 页面范围： 105120
- ISSN号： 0169-2607
- 关键字： Electrocardiogram (ECG); Myocardial infarction (MI); Dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT); Discrete wavelet packet transform (DWPT); Multilinear principal component analysis (MPCA)
- 摘要： Background and objective: It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm.
Methods: After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads x subbands x samples x beats) was constructed based on the discrete wavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To reduce the tensor dimension and preserve the intrinsic information, the multilinear principal component analysis (MPCA) was employed. Afterward, 84 discriminate features were fed into a classifier of bootstrap-aggregated decision trees (Treebagger). A total of 78 healthy and 328 MI (6 types) records including 57557 beats were chosen from PTB diagnostic ECG database for evaluation.
Results: The validation results demonstrated that our proposed MI detection and localization system embedded with Dual-Q TQWT and wavelet packet tensor decomposition outperformed commonly used discrete wavelet transform (DWT), empirical mode decomposition (EMD) denoising methods and vector-based PCA method. With the Treebagger classifier, we obtained an accuracy of 99.98% in beat level and an accuracy of 97.46% in record level training/testing for MI detection. We also achieved an accuracy of 99.87% in beat level and an accuracy of 90.39% in record level for MI localization.
Conclusion: Altogether, the automated system brings potential improvement in automated detection and localization of MI in clinical practice. (C) 2019 Elsevier B.V. All rights reserved.
