Fig. 1: Overview of the GIRAFFE framework for ECG arrhythmia classification and interpretability. | npj Digital Medicine

Fig. 1: Overview of the GIRAFFE framework for ECG arrhythmia classification and interpretability.

From: Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach

Fig. 1: Overview of the GIRAFFE framework for ECG arrhythmia classification and interpretability.

a Dataset split and training strategy: models are trained on different subsets with early stopping. b Ensemble creation using GIRAFFE: model predictions are fused using evolved strategies (mean, thresholding, weighted sum). c Generalization and performance metrics on the G and L datasets: Precision-Recall/ROC curves and probability distributions highlight GIRAFFE’s improvements. d Explainable artificial intelligence (XAI) interpretability: Local Interpretable Model-Agnostic Explanations (LIME) and Integrated Gradients (IG) highlight image regions; results are ensembled to showcase model reasoning.

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