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

Anita Ramachandran,Anupama Karuppiah

doi:10.3390/healthcare9070914

Anita Ramachandran, Anupama Karuppiah

Open Access

https://doi.org/10.3390/healthcare9070914

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Journal: Healthcare	Publication Date: Jul 20, 2021
Citations: 44	License type: CC BY 4.0

Affiliation: Birla Institute of Technology and Science, Pilani

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.

Highlights

This article is an open access articleSleep apnea is a sleep disorder in which a sleeping person’s breathing is disturbed
The focus technical focus of this study selected this study from technical of this study includes includes the following facets of sleep apnea detection: (i) type of sensors used for data the following facets of sleep apnea detection: (i) type of sensors used for data collection, collection, (ii) feature engineering the(iii) data, and (iii) used classifiers used (ii) feature engineering approachesapproaches applied on applied the data,on and classifiers for sleep for sleep apnea detection/classification
Feature selection was performed by discarding redundant features, leading to nine features being used for training decision trees, discriminant analysis, logistic regression, support vector machines, variation of k-nearest neighbor (kNN), and ensemble learning classifiers

Summary

Introduction

Sleep apnea is a sleep disorder in which a sleeping person’s breathing is disturbed. It is prevalent in adults as well as a small percentage of the juvenile population [1]. We review the recent state-of-the-art research in the application of machine learning for sleep apnea detection. (ii) feature engineering the(iii) data, and (iii) used classifiers used (ii) feature engineering approachesapproaches applied on applied the data,on and classifiers for sleep for sleep apnea detection/classification. This is diagnosed, and the biomedical parameters along with their derivatives that aid the is diagnosed, and the biomedical parameters along with their derivatives that aid ininthe process.

Background

The Need for More Accessible Detection Mechanisms—Sensors to the Aid

Classic Machine Learning Based Solutions

Deep Learning Based Solutions