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 F1 score (84.17%), and macro ROC-AUC (97.03%). The CNN-BiLSTM achieved the highest macro recall (87.37%), indicating stronger sensitivity to arrhythmic classes, while the CNN-GRU remained competitive with a macro recall of 86.85% and a macro F1 score of 83.13%.

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 F1 score and the CNN-BiLSTM the strongest macro recall, indicating improved class-balanced recognition of clinically relevant arrhythmic events. These results show that feature-based models remain highly competitive for smartwatch-based arrhythmia detection, achieving the strongest overall and macro F1 performance among the proposed methods, while hybrid convolutional-recurrent architectures provide increased sensitivity to minority rhythm classes. Deployment-oriented profiling under an identical single-core benchmark showed a mean end-to-end inference time of 33.08 ms for the Random Forest pipeline and 51.87 ms for the CNN-BiLSTM model on a 30-s segment, supporting near-real-time rhythm screening.

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.