Key result
The FCM-ETIFRST method achieved 98.95% accuracy on the MIT-BIH dataset and 98.45% accuracy on the CPCS 2018 dataset for arrhythmia classification.
Why the study?
Existing arrhythmia classification methods on ECG signals have limitations concerning imbalanced data and classification overfitting.
The proposed FCM-ETIFRST feature selection combined with ResNet-18 feature extraction achieves high accuracy for arrhythmia classification on standard ECG datasets.
May support AI arrhythmia classification on benchmarks; leaves open prospective clinical validation before adoption.
Arrhythmia classification on Electrocardiogram (ECG) signals is an important process for the diagnosis of cardiac disease and arrhythmia disease. The existing researches in arrhythmia classification have limitations of imbalance data problem and overfitting in classification. This research applies Fuzzy C-Means (FCM) – Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification. The selected features from FCM-ETIFRST were applied to the Multi-class Support Vector Machine (MSVM) for arrhythmia classification. The ResNet18 – Convolution Neural Network (CNN) was applied for feature extraction in input signal to overcome imbalance data problem. Conventional feature extraction along with CNN features are applied for FCM-ETIFRST feature selection process. The FCM-ETIFRST method in arrhythmia classification is evaluated on MIT-BIH and CPCS 2018 dataset. The FCM-ETIFRST has 98.95% accuracy and Focal loss-CNN has 98.66% accuracy on MIT-BIH dataset. The FCM-ETIFRST method has 98.45% accuracy and Explainable Deep learning Model (XDM) method have 93.6% accuracy on CPCS 2018 dataset.
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Rajeshwari et al. (2023) studied Arrhythmia. FCM-ETIFRST feature selection with MSVM and ResNet18-CNN vs. Focal loss-CNN and Explainable Deep learning Model (XDM) was evaluated on Classification accuracy. The FCM-ETIFRST method achieved 98.95% accuracy on the MIT-BIH dataset and 98.45% accuracy on the CPCS 2018 dataset for arrhythmia classification.
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