Key result
Novel machine learning pipeline achieves ~90% accuracy for cardiovascular disease classification.
Why the study?
Orthogonal Matching Pursuit algorithms have been underestimated in improving the performance of ECG signal classification for cardiovascular disease.
Does a machine learning pipeline using Orthogonal Matching Pursuit and classical classifiers accurately classify ECG signals into cardiac disease classes?
Does a machine learning pipeline using Orthogonal Matching Pursuit and classical classifiers accurately classify ECG signals into cardiac disease classes?
Using Orthogonal Matching Pursuit for feature extraction enables classical machine learning models like Decision Trees to achieve high accuracy in automated ECG interpretation.
Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Machine Learning classifiers were defined and investigated. The study was undertaken on the recently published PTB-XL database, whose ECG signals were previously subjected to detailed analysis. The classification was realized for class 2, class 5, and class 15 cardiac diseases. A new method of detecting R-waves and, based on them, determining the location of QRS complexes was presented. Novel aggregation methods of ECG signal fragments containing QRS segments, necessary for tests for classical classifiers, were developed. As a result, it was proved that ECG signal subjected to algorithms of R wave detection, QRS complexes extraction, and resampling performs very well in classification using Decision Trees. The reason can be found in structuring the signal due to the actions mentioned above. The implementation of classification issues achieved the highest Accuracy of 90.4% in recognition of 2 classes, as compared to less than 78% for 5 classes and 71% for 15 classes.
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Sandra Śmigiel (2022) studied Cardiovascular disease (ECG classification) (n=17,011). Machine learning with Orthogonal Matching Pursuit (OMP) algorithms was evaluated on Classification accuracy for 2, 5, and 15 cardiac disease classes. The proposed machine learning pipeline using Orthogonal Matching Pursuit achieved a classification accuracy of 90.4% for 2 classes, 77.66% for 5 classes, and 70.79% for 15 classes.
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