Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition. (February 2020) - Liu, Jia; Zhang, Chi; Zhu, Yongjie; Ristaniemi, Tapani; Parviainen, Tiina; Cong, Fengyu
Highlights: 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. Abstract: 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 × 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. 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.