A Review on Speech Biomarkers for Obstructive Sleep Apnea(OSA)

Abstract

Obstructive sleep apnea is well known as OSA. It is a breathing disorder that happens when we sleep, as it blocks significant parts of the upper airway(UA). The person becomes breathless or choked, resulting in loud snoring and dizziness throughout the day, even after proper sleep. If proper attention is not given, it may lead to high blood pressure(BP), heart strokes, etc. Mostly affecting people who are obese. Sleep apnea can be detected by analyzing the person’s snoring pattern, which helps diagnose the disease’s severity. Studies have been conducted on various databases which are publicly available in order to make a comparative analysis amongst various machine learning algorithms, iVectors, or supervectors. Along with the various feature extraction techniques which are already exploited by the various state-of-the-art algorithms. In this paper, we discussed several classical machine learning algorithms used for detecting OSA will be comprehensively reviewed whilst discussing the constraints that arise when machine learning algorithms are exploited. Since the emergence of wearable technology, alternative methods of diagnosing OSA have been investigated, including home sleep tests.