Interpretable RR Interval-Based ECG Rhythm Abnormality Detection: Cross-Dataset Evaluation, Feature Explainability, and Feature-Level Sensitivity Analysis

Background/Objectives: Interpretable ECG rhythm classification remains challenging because of variability in recording conditions, patient characteristics, and annotation practices across datasets. Although deep learning models can achieve high classification performance, their limited interpretability and sensitivity to domain shift can restrict their applicability in real-world clinical settings. This study investigates an interpretable and computationally efficient framework for ECG rhythm abnormality detection using statistical descriptors of RR interval variability. Methods: Mean RR, SDNN, RMSSD, pNN50, and heart rate features were extracted from ECG recordings to characterize beat-to-beat temporal variability. A Random Forest classifier was employed for binary rhythm classification using the PhysioNet/CinC 2017 dataset, followed by cross-dataset testing on the ECG-SDS dataset, which consists of synthetic ECG recordings. Same-dataset evaluation was additionally performed on ECG-SDS to establish a same-dataset performance benchmark. Feature ablation and feature-level sensitivity analyses using controlled Gaussian perturbations were conducted to examine feature contribution and classifier sensitivity to perturbations in the extracted feature representation. Multi-class experiments were also performed by incorporating atrial fibrillation and sinus tachy-brady rhythms. Results: In binary cross-dataset evaluation, the proposed framework achieved an accuracy of 78.6%, abnormal rhythm recall of 74.2%, and ROC-AUC of 0.836 on ECG-SDS. The same-dataset ECG-SDS evaluation achieved an accuracy of 93% and ROC-AUC of 0.89. Ten-fold stratified cross-validation yielded a mean accuracy of 92.20% ± 2.63% and mean AUC of 0.8880 ± 0.0477. Feature ablation showed only modest performance changes when individual descriptors were removed. The exploratory same-dataset multi-class evaluation on ECG-SDS achieved an accuracy of 91.67%. Conclusions: The results demonstrate that compact RR interval statistical descriptors can provide an interpretable and computationally efficient representation for ECG rhythm classification. The reduction in performance during cross-dataset testing may reflect dataset shift and differences in model configuration. These cross-dataset results should be interpreted as a technical assessment of transferability rather than as validation in an independent clinical population. Further validation using independent real-world clinical ECG datasets is required before conclusions regarding clinical generalizability or screening applications can be drawn.