Key result
StandardScaler and PCA preprocessing boosts machine learning accuracy for AF detection to 100%.
Why the study?
Automated detection of atrial arrhythmias such as atrial fibrillation and atrial flutter is complicated by complex changes in both ECG wave morphology and cardiac cycle time intervals.
Do machine learning anomaly detection algorithms with data preprocessing (StandardScaler and PCA) improve the classification accuracy of ECG signals for atrial arrhythmias?
Do machine learning anomaly detection algorithms with data preprocessing (StandardScaler and PCA) improve the classification accuracy of ECG signals for atrial arrhythmias?
Answers are grounded in this paper and cite their sources.
Data preprocessing via standardization and dimensionality reduction is critical for maximizing the accuracy of machine learning anomaly detection algorithms when classifying morphological ECG changes in atrial fibrillation.
The article presents an experimental study of the effectiveness of machine learning methods for classifying electrocardiographic signals by rhythmic and morphological features using information tech- nology based on the mathematical apparatus of cyclic random processes. The problem of automated detection of atrial arrhythmias is considered, particularly atrial fibrillation and atrial flutter, which are characterized by complex changes in both ECG wave morphology and cardiac cycle time intervals. Four anomaly detection algorithms were investigated for classifying pathological conditions: OneClassSVM with radial basis function, IsolationForest, Local Outlier Factor (LOF), and Elliptic- Envelope. The impact of data preprocessing methods (StandardScaler for standardization and PCA for dimensionality reduction) on classification accuracy was analyzed. Experimental results showed that preprocessing is critically important for morphological disorder classification, increasing accuracy from 50-83 % to 100 % for atrial fibrillation. The LOF algorithm demonstrated the most stable results (83-100%) for different types of pathologies. For rhythm disorder classification, the IsolationForest, LOF, and EllipticEnvelope methods showed equally high efficiency (89 %), while preprocessing did not lead to significant improvement in results.
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Mosiy et al. (2025) studied Atrial arrhythmias (atrial fibrillation and atrial flutter). Machine learning anomaly detection algorithms (OneClassSVM, IsolationForest, Local Outlier Factor, EllipticEnvelope) with data preprocessing (StandardScaler and PCA) vs. Algorithms without data preprocessing / comparison among the four algorithms was evaluated on Classification accuracy for morphological and rhythm disorders in ECG signals. Data preprocessing with StandardScaler and PCA increased machine learning classification accuracy for atrial fibrillation morphological disorders from 50-83% to 100%.
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