Ambulatory monitoring of ECG signals for arrhythmia detection using deep neural networks

Reliable arrhythmia detection plays a pivotal role in preventing adverse cardiac events, yet the earliest warning signs often manifest as subtle, patient‑specific perturbations in the electrocardiogram~(ECG). This study introduces a multiscale wavelet framework that converts modified lead‑II segments from the MIT–BIH Arrhythmia Database into time–frequency scalograms via the Continuous Wavelet Transform. Real and complex Morlet, Mexican Hat, and first‑order Gaussian wavelets are evaluated over low, mid, high and full frequency bands. The resulting images are processed under strict inter‑patient splits by state‑of‑the‑art backbones—ResNet‑50, DenseNet‑121, EfficientNet‑B0 and ViT‑B/16. In the five‑class (non‑AAMI) setting, the best hybrid configuration attains {83.0\,\%} overall accuracy while boosting minority‑class sensitivity by nearly 20 percentage points. Multiscale augmentation with Mexican Hat and Morlet scalograms elevates DenseNet‑121 accuracy from \(\sim60\,\%\) to {80.6\,\%}. Under the four‑class AAMI taxonomy Normal (N), Supraventricular (S), Ventricular (V), and Fusion (F), the same strategy—combined with a class‑weighted loss—yields {88–90\,\%} accuracy when models are trained on one patient subset and evaluated on the other, demonstrating robust generalization. These findings indicate that wavelet‑based time–frequency representations, when paired with modern deep networks, deliver tangible improvements for ambulatory ECG monitoring. Future work will address real‑time wearable deployment, adaptive scale selection, transfer learning from larger ECG corpora and ultra‑low‑latency inference.