The use of smartwatch photoplethysmography (PPG) data for arrhythmia detection presents several limitations:
Motion Artifacts: Smartwatch PPG data is susceptible to motion artifacts, which can distort the PPG waveform and lead to misclassification of arrhythmias. Future research can focus on developing more robust algorithms for motion artifact detection and correction, possibly using advanced signal processing techniques or machine learning models trained specifically to identify and mitigate these artifacts.
Lower Signal-to-Noise Ratio (SNR): Compared to fingertip PPG data, smartwatch PPG often has a lower SNR, which can hinder accurate arrhythmia detection. Future studies could explore the integration of additional sensors (e.g., ECG or accelerometers) to enhance the quality of the PPG signal and provide complementary data for more accurate classification.
Limited Training Data: The availability of labeled training data for PAC/PVC is often limited, which can affect model performance. Future research should focus on collecting larger and more diverse datasets that include a wide range of arrhythmia cases, particularly in real-life settings, to improve model generalizability.
User Variability: Individual differences in physiology, skin tone, and smartwatch fit can affect PPG signal quality. Future studies could investigate personalized algorithms that adapt to individual user characteristics, potentially improving detection accuracy across diverse populations.
Real-time Processing Constraints: The computational efficiency of the model is crucial for real-time applications. Future research should continue to optimize model architectures and algorithms to ensure they can run efficiently on wearable devices without sacrificing performance.
By addressing these limitations through targeted research and development efforts, the effectiveness of smartwatch PPG data for arrhythmia detection can be significantly improved.