# A Survey on Recent Advances in Machine Learning Based Sleep Apnea Detection Systems

## TL;DR

This paper reviews recent machine learning approaches to detect sleep apnea, aiming to make diagnosis more accessible and affordable.

## Contribution

The paper provides a comprehensive survey of machine learning techniques and sensor-based systems for sleep apnea detection.

## Key findings

- Machine learning and sensor fusion are increasingly used for sleep apnea detection.
- Various sensors and feature engineering methods are explored for data collection and analysis.
- Challenges in system design include data variability and classifier accuracy.

## Abstract

Sleep apnea is a sleep disorder that affects a large population. This disorder can cause or augment the exposure to cardiovascular dysfunction, stroke, diabetes, and poor productivity. The polysomnography (PSG) test, which is the gold standard for sleep apnea detection, is expensive, inconvenient, and unavailable to the population at large. This calls for more friendly and accessible solutions for diagnosing sleep apnea. In this paper, we examine how sleep apnea is detected clinically, and how a combination of advances in embedded systems and machine learning can help make its diagnosis easier, more affordable, and accessible. We present the relevance of machine learning in sleep apnea detection, and a study of the recent advances in the aforementioned area. The review covers research based on machine learning, deep learning, and sensor fusion, and focuses on the following facets of sleep apnea detection: (i) type of sensors used for data collection, (ii) feature engineering approaches applied on the data (iii) classifiers used for sleep apnea detection/classification. We also analyze the challenges in the design of sleep apnea detection systems, based on the literature survey.