A1 Journal article (refereed)
Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition (2020)
Liu, J., Zhang, C., Zhu, Y., Ristaniemi, T., Parviainen, T., & Cong, F. (2020). Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition. Computer Methods and Programs in Biomedicine, 184, Article 105120. https://doi.org/10.1016/j.cmpb.2019.105120
JYU authors or editors
Publication details
All authors or editors: Liu, Jia; Zhang, Chi; Zhu, Yongjie; Ristaniemi, Tapani; Parviainen, Tiina; Cong, Fengyu
Journal or series: Computer Methods and Programs in Biomedicine
ISSN: 0169-2607
eISSN: 1872-7565
Publication year: 2020
Volume: 184
Article number: 105120
Publisher: Elsevier B.V.
Publication country: Netherlands
Publication language: English
DOI: https://doi.org/10.1016/j.cmpb.2019.105120
Publication open access: Not open
Publication channel open access:
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65924
Additional information: Corrigendum to this article: https://doi.org/10.1016/j.cmpb.2020.105785
Abstract
Methods. After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on thediscretewavelet 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 (6types) records including 57557 beats were chosen from PTB diagnostic ECG database for evaluation.
Results.The validation results demonstratedthat our proposed MI detection and localization system embedded with Dual-Q TQWT and wavelet packet tensor decomposition outperformedcommonly 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 detectionand localization of MI in clinical practice.
Keywords: signal processing; signal analysis; ECG; myocardial infarction
Free keywords: 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)
Contributing organizations
Ministry reporting: Yes
VIRTA submission year: 2020
JUFO rating: 1
