۱- Introduction

۲- Database

۳- Methods

۴- Results and discussion

۵- Conclusion

References

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 bootstrapaggregated 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 vectorbased 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.

Introduction

Myocardial infarction (MI) is defined as myocardial cell death due to prolonged ischemia [1]. As one of the main causes of death and disability, MI is an intractable disease and can result in artery disease. In clinical practice, many techniques, including electrocardiographic (ECG), biochemical markers, imaging and so on, are used to assist in the diagnosis of MI. Among these techniques, the non-invasive ECG, an economic tool, is widely used in MI detection [2,3]. The ECG abnormalities of MI can be observed in the PR segment, the QRS complex, the ST segment or the T wave [1]. However, the diagnosis of MI usually requires multiple ECGs because the ECG signals are time-varying in nature with small amplitude. Manual inspection in clinical practice is not only timeconsuming and strenuous but also leads to inter- and intraevaluator variability [4,5]. Therefore, a computer-aided diagnosis system (CADS) of MI should be developed to realize time-saving and reliable analysis [6–۱۱]. Good quality ECG is a guarantee of reliable CADS, while the ECG signals are often corrupted by noise [12]. The ECG signals are usually mixed with different kinds of artifacts, such as power line interference, muscle artifacts, and baseline drifts. Therefore, it is necessary to remove artifacts by implanting denoising method in CADS.