Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using Classification‑Based Feature Selection

Background: Cardiovascular disease (CVD) is the first cause of world death, and myocardial infarction (MI) is one of the five primary disorders of CVDs which the patient electrocardiogram (ECG) analysis plays a dominant role in MI diagnosis. This research aims to evaluate some extracted features of ECG data to diagnose MI. Methods: In this paper, we used the Physikalisch‑Technische Bundesanstalt database and extracted some morphological features, such as total integral of ECG, integral of the T‑wave section, integral of the QRS complex, and J‑point elevation from a cycle of normal and abnormal ECG waveforms. Since the morphology of healthy and abnormal ECG signals is different, we applied integral to different ECG cycles and intervals. We executed ۱۰۰ of iterations on a ۱۰‑fold and ۵‑fold cross‑validation method and calculated the average of statistical parameters to show the performance and stability of four classifiers, namely logistic regression (LR), simple decision tree, weighted K‑nearest neighbor, and linear support vector machine. Furthermore, different combinations of proposed features were employed as a feature selection procedure based on classifier’s performance using the aforementioned trained classifiers. Results: The results of our proposed method to diagnose MI utilizing all the proposed features with an LR classifier include ۹۰.۳۷%, ۹۴.۸۷%, and ۸۶.۴۴% for accuracy, sensitivity, specificity, respectively. Also, we calculated the standard deviation value for the accuracy of ۰.۰۰۶. Conclusion: Our proposed classification‑based method successfully classified and diagnosed MI using different combinations of presented features. Consequently, all proposed features are valuable in MI diagnosis and are praiseworthy for future works.

Keywords:

Biological signal processing , classification , cross‑validation , electrocardiography , feature selection , linear support vector machine , myocardial infarction , simple tree , weighted K‑nearest neighbor