To enhance the sensitivity for premature atrial contractions (PAC) and premature ventricular contractions (PVC) detection while maintaining accuracy for other arrhythmia classes, several strategies can be employed:
Data Augmentation: Implementing data augmentation techniques can help create a more balanced dataset. This could involve generating synthetic PAC/PVC signals using techniques like SMOTE (Synthetic Minority Over-sampling Technique) or GANs (Generative Adversarial Networks) to increase the representation of these classes in the training dataset.
Advanced Feature Engineering: Incorporating additional features derived from the PPG signals, such as heart rate variability (HRV) metrics, could provide more context for the model. Features that capture the temporal dynamics of the heart rate and the morphology of the PPG waveform may enhance the model's ability to distinguish PAC/PVC from normal sinus rhythm (NSR) and atrial fibrillation (AF).
Ensemble Learning: Utilizing an ensemble of models, where multiple algorithms are trained and their predictions combined, could improve sensitivity. Different models may capture various aspects of the data, and their collective output could lead to better detection rates for PAC/PVC.
Transfer Learning: Leveraging pre-trained models on larger datasets for initial training could help the model learn more generalized features before fine-tuning on the specific Pulsewatch dataset. This approach can be particularly beneficial if the pre-trained models are trained on similar tasks.
Hyperparameter Optimization: Conducting a thorough hyperparameter tuning process using techniques like grid search or Bayesian optimization can help identify the optimal settings for the model, potentially leading to improved sensitivity for PAC/PVC detection.
Incorporating Temporal Context: Enhancing the model architecture to include more sophisticated recurrent layers or attention mechanisms could allow the model to better capture the temporal dependencies in the PPG signals, which are crucial for detecting subtle arrhythmia patterns.
By implementing these strategies, the proposed model could achieve higher sensitivity for PAC/PVC detection while preserving accuracy for other arrhythmia classes.