Imbalanced Learning Study on Atrial Flutter using Active Learning - de Almeida, A.; Nunes, Luis; Moreira, A. (RECPAD 2025 - 31st Portuguese Conference on Pattern Recognition., 2025)
This study investigates the detection of Atrial Flutter in highly imbalanced ECG datasets. Using the XGBoost classifier, multiple resampling techniques, anomaly detection methods and Active Learning strategies were evaluated. Synthetic data generated by GANs significantly improved minority-class recognition, enhancing recall and F1-score. With emphasis on Active Learning algorithms, certainty-based and FIMPS, iteratively refined the model on the most uncertain samples. Results demonstrate that combining GAN augmentation with Active Learning outperforms traditional resampling methods. This approach highlights the potential for more accurate and efficient automated ECG analysis.