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Evaluating Morphological Features of Electrocardiogram Signals for Diagnosing of Myocardial Infarction Using
Seyed Ataddin Mahmoudinejad1, Naser Safdarian2,3
1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Insights
This study evaluated electrocardiogram (ECG) features for diagnosing myocardial infarction (MI), a leading cause of death. The proposed method achieved high accuracy, demonstrating the value of ECG analysis in cardiovascular disease detection.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Cardiovascular disease (CVD) is the leading global cause of mortality.
- Myocardial infarction (MI) is a critical CVD, with electrocardiogram (ECG) analysis vital for diagnosis.
- This research focuses on evaluating extracted ECG features for MI diagnosis.
Purpose of the Study:
- To assess the diagnostic capability of specific morphological ECG features for myocardial infarction.
- To determine the effectiveness of various feature combinations in improving MI detection accuracy.
- To evaluate the performance and stability of different machine learning classifiers for MI diagnosis.
Main Methods:
- Extracted morphological ECG features including total integral, T-wave integral, QRS integral, and J-point elevation from the Physikalisch-Technische Bundesanstalt database.
- Applied integral analysis to normal and abnormal ECG waveforms across different cycles and intervals.
- Utilized 10-fold and 5-fold cross-validation with 100 iterations, evaluating logistic regression, decision trees, K-nearest neighbors, and support vector machines.
- Performed feature selection based on classifier performance with different feature combinations.
Main Results:
- The logistic regression classifier, using all proposed features, achieved 90.37% accuracy, 94.87% sensitivity, and 86.44% specificity.
- The standard deviation for accuracy was calculated to be 0.006, indicating high stability.
- Feature combinations were evaluated, showing the value of proposed features in MI diagnosis.
Conclusions:
- The proposed classification-based method effectively diagnosed MI using various feature combinations.
- All evaluated ECG features demonstrated significant value for MI diagnosis.
- The findings support the utility of these features for future research and clinical applications in cardiovascular disease detection.
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 100 of iterations on a 10-fold and 5-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 90.37%, 94.87%, and 86.44% for accuracy, sensitivity, specificity, respectively. Also, we calculated the standard deviation value for the accuracy of 0.006.
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.
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