Contrastive Self-Supervised Learning Based Approach for Patient
  Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
Subangkar Karmaker Shanto Shoumik Saha Atif Hasan Rahman Mohammad Mehedy Masud Mohammed Eunus Ali
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.