Pulse Deficit in Photoplethysmography as an Indicator for Atrial Fibrillation

Atrial Fibrillation is the most common cardiac arrhythmia in adults and is associated with an increased risk of stroke and other cardiovascular diseases. Early detection of atrial fibrillation is crucial for timely intervention and improved patient outcomes. While electrocardiography is the clinical gold standard for atrial fibrillation detection, it is not suitable for long-term and home monitoring due to its requirement for electrode-skin contact. Photoplethysmography, on the other hand, offers a more convenient alternative for continuous monitoring using wearable devices. This study investigates the reliability of the pulse deficit in photoplethysmography signals as an indicator for atrial fibrillation. We employ a wavelet transformation to enhance the visibility of the pulse deficit in photoplethysmography signals and train a deep neural network to classify atrial fibrillation based on these transformed signals. A five-fold cross validation revealed an average AUC of 0.975 and an F1 score of 0.935, indicating a high level of accuracy and reliability. The networks's predictions are further investigated using the gradient-weighted class activation mapping approach to validate, whether the classification is based on the pulse deficit. Our work succefully proves that the pulse deficit in photoplethysmography signals can serve as a robust indicator for atrial fibrillation. Nevertheless, the impact of other heart rate-related characteristics on the classification could not be entirely excluded.