A Lightweight, CPU-Deployable, and Interpretable ECG Arrhythmia Classification Pipeline Using the MIT-BIH Arrhythmia Database

Electrocardiogram (ECG) arrhythmia classification is a foundational task in medical artificial intelligence, yet many high-performing deep learning approaches require GPU resources and may be difficult to interpret in clinical settings. This study presents a lightweight, CPU-deployable, and interpretable pipeline for heartbeat-level arrhythmia classification using the MIT-BIH Arrhythmia Database. ECG beats were segmented around annotated R-peaks and mapped into five AAMI-style aggregated classes (N, S, V, F, Q). We extracted compact time-domain statistics and frequency-domain energy features, then trained and compared three classical machine learning models: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). Using a record-wise split to reduce data leakage risk, RF achieved the highest accuracy (0.864), while LR provided the strongest one-vs-rest ROC-AUC (0.806). Class-wise ROC curves and feature-importance analysis suggested that spectral energy and amplitude-related statistics contributed substantially to discrimination. Overall, the results demonstrate that interpretable, resource-efficient ECG classification remains feasible without deep networks, supporting practical deployment in CPU-only environments and rapid prototyping of medical AI systems.