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

arXiv: https://arxiv.org/abs/2308.02433 · HTML: https://mlsanity.com/paper/2308.02433/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 22 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Self-supervised Learning is More Robust to Dataset Imbalance](https://mlsanity.com/paper/2110.05025/) (2021)
- [Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records](https://mlsanity.com/paper/1709.01648/) (2017)
- [Evaluating surgical skills from kinematic data using convolutional neural networks](https://mlsanity.com/paper/1806.02750/) (2018)
- [Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1805.09133/) (2018)
- [ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease](https://mlsanity.com/paper/2010.08190/) (2020)
- [End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables](https://mlsanity.com/paper/1807.10707/) (2018)
- [Patient Similarity Analysis with Longitudinal Health Data](https://mlsanity.com/paper/2005.06630/) (2020)
- [Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders](https://mlsanity.com/paper/1805.03473/) (2018)

## Built on by (most cited first, 1 of 1)

- [Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals](https://mlsanity.com/paper/2406.08930/) (2024)

## Abstract (arXiv)

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.