Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition

• Published in: Computer methods and programs in biomedicine 2020-02, Vol.184, p.105120-105120, Article 105120
• Main Authors: Liu, Jia, Zhang, Chi, Zhu, Yongjie, Ristaniemi, Tapani, Parviainen, Tiina, Cong, Fengyu
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Case-Control Studies; Discrete wavelet packet transform (DWPT); Dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT); Electrocardiogram (ECG); Electrocardiography - methods; Humans; Multilinear principal component analysis (MPCA); Myocardial infarction (MI); Myocardial Infarction - diagnosis; Myocardial Infarction - physiopathology; Principal Component Analysis; Reproducibility of Results; Signal Processing, Computer-Assisted; Wavelet Analysis
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2019.105120

•The myocardial infarction (MI) detection and localization system in single-beat was developed based on non-invasive ECG.•A novel ECG denoising method dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) was introduced.•We realized the processes of feature extraction and dimensionality reduction with discrete wavelet packet transformation (DWPT) and multilinear principal component analysis (MPCA).•A total of 78 healthy and 328 MI (6 types: AMI, ALMI, IMI, ASMI, ILMI, IPLMI) records were chosen from PTB diagnostic ECG database for evaluation.•With the Treebagger classifier, we obtained good results considering both beat-level and record-level for MI detection and localization. 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. After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × 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. 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. Altogether, the automated system brings potential improvement in automated detection and localization of MI in clinical practice.