Key result
SVM model with feature selection classifies AFib on ECG with ~0.95 F1 score.
Why the study?
Most wearable single-lead ECG devices for detecting AF rely on high-computation algorithms, which increases device costs.
Does a hybrid Support Vector Machine approach accurately classify ECG signals into Atrial Fibrillation and Normal?
Does a hybrid Support Vector Machine approach accurately classify ECG signals into Atrial Fibrillation and Normal?
A hybrid SVM approach with feature selection can accurately and efficiently classify ECG signals for AFib detection, potentially reducing computational complexity for wearable devices.
Atrial Fibrillation (AFib) is an important cardiac arrhythmia marked by an irregular and frequently accelerated heartbeat which increases the risk of stroke and other cardiovascular complications. Detecting AFib promptly allows for timely intervention along with the treatments like Cardioversion therapy, medications and Surgery or catheter procedures might help mitigate these risks. Wearable devices and mobile health applications make use of single-lead ECG, allowing for immediate identification and response but most of the devices are based on high computation algorithms leading to a rise in the overall cost of devices. The proposed methodology automatically classifies ECG signals into AFib and Normal using Machine Learning algorithm. To increase the diversity in the dataset, this study has considered 5822 ECG signals from two prominent databases, steps like feature engineering based on Variable Clustering were used to handle multicollinearity. Different Machine Learning (ML) models were assessed, encompassing Random Forest, Logistic Regression, SVM, and Adaboost. Among these, SVM emerged as the preferred choice due to its outstanding performance, achieving a F1 score of 0.95. The reliability of this evaluation was confirmed through a 10-fold cross validation method and hypertuning the parameters using the Grid Search method. This study can bridge the gap in Atrial Fibrillation diagnosis and potentially revolutionize healthcare by offering trustworthy and accessible diagnostic procedures.
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Dsouza et al. (2024) studied Atrial Fibrillation (n=5,822). Support Vector Machine (SVM) algorithm vs. Random Forest, Logistic Regression, and Adaboost was evaluated on F1 score for AFib classification. A Support Vector Machine model integrating feature selection successfully classified ECG signals into Atrial Fibrillation and Normal rhythms, achieving an F1 score of 0.95.
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