Enhancing atrial fibrillation detection in PPG analysis with sparse labels through contrastive learning

Abstract

Background

With the advancements in wearable technology, photoplethysmography (PPG) has emerged as a promising technique for detecting atrial fibrillation (AF) due to its ability to capture cardiovascular information. However, current deep learning-based methods has strict requirements on the quantity of labeled data. To overcome this limitation, we explore the performance of self-supervised contrastive learning in PPG-based AF detection.

Methods

Our method initially utilizes 1,209 h of unlabeled PPG data from the VitalDB database, conducting self-supervised pretraining using two contrastive learning frameworks, SimCLR and BYOL. Subsequently, the weights of the encoder are transferred and fine-tuned on a small amount of labeled PPG data to complete the AF detection task, including the selected MIMIC III, UMass, and DeepBeat datasets. In the realm of contrastive learning, we investigated seven data augmentation operations to explore their composite and preferred combinations, as well as the effects of double-sided and single-sided transformations.

Results

Our research ultimately demonstrated that the preferred combination, incorporating single-sided transformation with the Drift operation, is most suitable for PPG data. Notably, even with only 1 %, 20 %, and 1 % of the training data from the three datasets used for fine-tuning, our approach achieves better F1 scores compared to supervised learning on the respective complete training sets. Additionally, on the 0.01 % DeepBeat training set, fine-tuning still showed a clear advantage over supervised learning.

Conclusion

Appropriate self-supervised contrastive pretraining effectively leverages a substantial amount of existing unlabeled PPG data, thus reducing the reliance on labeled data for AF detection, and offering a possible solution to address the limitations posed by the scarcity of labels.