Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
This work addresses patient similarity analysis for healthcare applications, specifically atrial fibrillation detection, but is incremental as it applies known contrastive learning techniques to a new domain.
The paper tackles the problem of patient similarity search using physiological signals by proposing a contrastive self-supervised learning framework, achieving improved performance in atrial fibrillation detection from PPG signals compared to baseline methods on a dataset of over 170 individuals.
In this paper, we propose a novel contrastive learning based deep learning framework for patient similarity search using physiological signals. We use a contrastive learning based approach to learn similar embeddings of patients with similar physiological signal data. We also introduce a number of neighbor selection algorithms to determine the patients with the highest similarity on the generated embeddings. To validate the effectiveness of our framework for measuring patient similarity, we select the detection of Atrial Fibrillation (AF) through photoplethysmography (PPG) signals obtained from smartwatch devices as our case study. We present extensive experimentation of our framework on a dataset of over 170 individuals and compare the performance of our framework with other baseline methods on this dataset.