scispace - formally typeset
Open AccessJournal ArticleDOI

A Comprehensive Analysis on the Efficient Mechanisms to Detect Obstructive Sleep Apnea Using AI and Heuristic Algorithms

TLDR
A comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.
Abstract
Obstructive sleep apnea is a common problem arising in adults and children nowadays, determined by abnormalities in breathing gaps or incapability of air intake capacity during sleeping results in a decrease in oxygen level in blood. The brain detects this sudden decrease in the level of oxygen and sends a signal to wake the person up. Studies revealed the breathing stops for almost 10 seconds during a sleep apnea episode. There is no restriction on who can develop Obstructive Sleep Apnea(OSA), it can affect adults as well as infants. Our research primarily aims at assessing the various recent developments and studies made as a solution to this alarming problem. Their methodology and techniques have been studied and accuracy and sensitivity rates compared. A comprehensive and detailed study has been conducted on several research papers and studies done in the field of predicting sleep apnea. Sleep Apnea and classification of apneic signals have been mentioned in our study. The related researches have been studied extensively and compiled in our research work. The various techniques used by the researchers have been studied and tabulated along with the algorithm accuracies. It is observed that signal measurement along with AI algorithms has made significant advancements in OSA prediction. It is observed that Self Developed Algorithm on VAD showed the highest accuracy of 97%. PPG signal analysis and binary classification algorithm showed good accuracies of 86.67% and 86% respectively. AdaBoost, Decision Table and Bagging REPTree and SVM classifier also showed good accuracy of around 83% in the detection of Sleep Apnea episodes. The study highlighted the research works done to combat the rising problem of Obstructive Sleep Apnea. This comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.

read more

Content maybe subject to copyright    Report

References
More filters
Journal ArticleDOI

Real-time apnea-hypopnea event detection during sleep by convolutional neural networks

TL;DR: This paper proposes a new approach for real-time apnea-hypopnea event detection using convolutional neural networks and a single-channel nasal pressure signal and shows results that could potentially be used as a supportive method to reduce event detection time in sleep laboratories.
Proceedings ArticleDOI

A real-time auto-adjustable smart pillow system for sleep apnea detection and treatment

TL;DR: A smart phone-based auto-adjustable pillow system, which enables both sleep apnea detection and treatment, and a real-time feedback pillow adjustment algorithm to decide whether the pillow should be adjusted or not, is proposed and implemented.
Journal ArticleDOI

A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features

TL;DR: A neural network (NN) is developed and validated using SpO2 measurements obtained from pulse oximetry to predict OSA with a high performance and improved accuracy, which is better than reported techniques in the literature.
Journal ArticleDOI

Apnea Detection based on Respiratory Signal Classification

TL;DR: An automated approach towards identifying the presence of sleep apnea based on the acoustic signal of respiration is introduced and the VAD algorithm is used as a predictive tool for the segmentation of breath into sound and silence segments.
Journal ArticleDOI

Detection of airway obstructions and sleep apnea by analyzing the phase relation of respiration movement signals

TL;DR: A novel computer-aided diagnostic method of sleep apnea syndrome, a very common respiration disorder, that processes only the thoracic and abdominal excursion signals and can distinguish between obstructive and central episodes of apnea.
Related Papers (5)