ECG arrhythmia-type classification is an important task in cardiovascular healthcare. The imbalance among the classes in ECG datasets constitutes a major hurdle for the development of an accurate classification model, especially for rare idiosyncratic arrhythmia types relevant clinically. The paper presents a novel method using generative adversarial networks to mitigate class imbalance in the MIT-BIH Arrhythmia Database. We create an LSTM-GAN-based model to generate synthetic ECGs for minority classes, with which we create a balanced dataset for training. We compare several algorithms (LightGBM, XGBoost, Random Forest) on the original imbalanced dataset versus the Balanced Dataset obtained after GANs augmentation. Our findings indicate substantial enhancement in the classification performance across all metrics, with extremely favorable outcomes for minority arrhythmia class detection. Using the balanced dataset, the macro-averaged F1-score is improved by 27.3%, underscoring the use of GAN-based data augmentation in overcoming class imbalance in medical datasets. This method has vast implications for building more accurate and dependably arrhythmia detection systems in clinical practice.
Adversarial training, EDA (Easy Data Augmentation), class imbalance, going to get some rhythm into cardiac arrhythmia classification and ECG analysis, Machine Learning with LightGBM, XGBoost, and Random Forest
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