<![CDATA[Automated electrocardiogram (ECG) classification remains challenging because waveform-based deep-learning models capture rich morphology, whereas clinically relevant conduction and timing intervals are not always represented explicitly. This study investigates whether a compact interval-informed representation can retain useful discriminative information for ten ECG beat and event categories. MIT-BIH Arrhythmia Database recordings were filtered and divided into seven-second segments. RR, PR, and QT intervals and QRS width were summarized using minimum, maximum, mean, median, skewness, and kurtosis, yielding 24 features. To prevent information leakage, the original samples were partitioned before oversampling; feature scaling and SMOTE were applied only to training data, while validation and held-out test data retained their original distributions. Leakage-controlled five-fold cross-validation yielded 92.15% ± 0.43% accuracy and 86.78% ± 0.68% macro-F1. On the untouched 826-sample test set, the model achieved 92.62% accuracy, 87.49% macro-F1, and 92.68% weighted F1. The most frequent bidirectional errors occurred between Normal and Premature Ventricular Contraction beats. These results support the use of interval-statistical features as a compact representation for multicategory ECG analysis, while showing that rare-class performance and fiducial reliability remain limiting factors for broader clinical use.]]>
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