Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal

Autor:	Shanto, Subangkar Karmaker, Saha, Shoumik, Rahman, Atif Hasan, Masud, Mohammad Mehedy, Ali, Mohammed Eunus
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 4 figures, Preprint submitted to Journal of Computers in Biology and Medicine
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.02433 Zobrazit plný text záznamu View this record from Arxiv