Class imbalance among arrhythmia types and electrocardiogram (ECG) signal complexity present significant challenges for automated ECG-based arrhythmia detection. This research proposes an innovative approach that combines Genetic Algorithm (GA) optimization of Convolutional Neural Network (CNN) hyperparameters with morphological skeletonization of ECG images. The MIT-BIH Arrhythmia Database served as the primary data source, with the ECG signal converted to skeletonized representations emphasizing QRS complex geometry. A GA-optimized model was compared against a heuristic (manual design) baseline to determine optimal kernel and filter configurations. Evaluation emphasized not only overall accuracy but also robust metrics for minority classes. The optimized model achieved 97.26% accuracy, with macro recall improving substantially from 77.36% to 83.10% (+5.74%). These results demonstrate that evolutionary optimization enhances detection sensitivity to subtle geometric patterns, effectively mitigating class imbalance without artificial oversampling techniques.
Garza et al. (Fri,) studied this question.