Robustness vs. Efficiency in ECG Classification: A Comparative Study of Deep Learning and Classical Machine Learning Architectures

Purpose: Cardiovascular diseases remain the predominant cause of global mortality, necessitating reliable automated tools for early diagnosis. While Deep Learning (DL) has shown superiority in medical imaging, recent studies suggest that Classical Machine Learning models may offer comparable accuracy with lower computational costs. This study investigates this accuracy versus robustness trade-off. Methods: We present a rigorous comparison between modern Efficient Deep Learning architectures (MobileNetV4, GhostNetV2) and established Classical Baselines (CatBoost, Random Forest) for Myocardial Infarction detection using the PTB-XL dataset. Models were first evaluated on 2D scalograms generated via Continuous Wavelet Transform (CWT). To test structural robustness and artifact reliance, we subsequently introduced a 1D Raw Signal Benchmark evaluating the models on unaligned, raw time-series data. Results: On aligned 2D image data, Classical models achieved competitive F1-scores (~0.80). However, the 1D Raw Signal Benchmark revealed a severe performance degradation for Classical methods (F1 drop to ~0.04) on unaligned data, highlighting a heavy reliance on spatial artifacts. Conversely, Convolutional Neural Networks (CNNs) maintained high performance (F1 ~0.84) on raw signals. Conclusion: While Classical models are computationally efficient for highly structured data, they are structurally brittle. Efficient Deep Learning architectures, specifically MobileNetV4, offer the superior translation-invariant feature learning required for clinical signal analysis, delivering a 2.0x speedup over heavy CNN baselines with negligible accuracy loss.