Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Abstract

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

Development of advanced artificial intelligence techniques for the detection of myocardial infarction ECG signals in clinical settings

“…Clinically, myocardial enzymes are useful indicators for MCI diagnosis. However, during the early rescue period of acute MCI, the myocardial enzymes are usually not raised enough to provide any clinical indication [125]. Electrocardiogram (ECG) signals reflect the heart's electrical activity, wherein the ECG waveform alters during the occurrence of MCI [126].…”

“…However, as the ECG alterations represent small amplitudes for short durations, and the visual interpretation of the signals by clinicians is challenging and subjective, computer information technology [125] has been vehemently used for the automatic analysis of ECG signals for the detection of heart diseases, in recent years [126]. Machine learning techniques are mainly branched into conventional feature engineering and advanced deep learning techniques.…”