A Systematic Review of Detecting Sleep Apnea Using Deep Learning

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2019 Nov 16

PMID 31726771

Citations 37

Authors

Sheikh Shanawaz Mostafa

Fabio Mendonca

Antonio G Ravelo-Garcia

Fernando Morgado-Dias

Affiliations

Soon will be listed here.

Abstract

Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008-2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.

Citing Articles

Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques.

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Sleep Apnea Events Recognition Based on Polysomnographic Recordings: A Large-Scale Multi-Channel Machine Learning approach.

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