Development of an efficient deep learning model for arrhythmia detection from consumer smartwatches.

Introduction: Cardiac arrhythmias often progress without clear symptoms and may remain undetected until severe complications occur. Continuous monitoring through consumer smartwatches offers a scalable and non-invasive opportunity for real-time rhythm screening in free-living conditions. However, wrist-based photoplethysmography (PPG) signals are highly susceptible to motion artifacts and inter-subject variability, limiting traditional signal-processing techniques. In this work, we compare classical machine learning and deep learning approaches for multi-class arrhythmia classification from real-life smartwatch PPG and accelerometer recordings collected in the Pulsewatch clinical trial. Three rhythm categories are considered: normal sinus rhythm (NSR), atrial fibrillation (AF), and premature atrial or ventricular contractions (PAC/PVC). Methods: The evaluated approaches include a Random Forest classifier based on engineered temporal and morphological features, together with two end-to-end architectures, CNN-GRU and CNN-BiLSTM, designed to learn temporal patterns directly from raw two-channel smartwatch waveforms while addressing motion artifacts, sensor noise, and cross-subject generalization. Results: Under two-fold subject-independent cross-validation, the Random Forest achieved the highest average accuracy among the proposed methods (91.22%), together with the best macro precision (85.89%), macro F Discussion: Compared with the Pulsewatch baselines reported by Han et al., the proposed methods improved the available macro-level metrics, with the Random Forest obtaining the strongest macro precision and macro F Conclusion: Overall, this work highlights the trade-offs between the analyzed approaches in terms of accuracy, class-balanced performance, computational complexity, and deployment feasibility for wearable cardiac monitoring systems.