TY - JOUR
T1 - Deep and shallow learning model-based sleep apnea diagnosis systems
T2 - A comprehensive study
AU - Raisa, Roksana Akter
AU - Rodela, Ayesha Siddika
AU - Yousuf, Mohammad Abu
AU - Azad, Akm
AU - Alyami, Salem A.
AU - Lio, Pietro
AU - Islam, Md Zahidul
AU - Pogrebna, Ganna
AU - Moni, Mohammad Ali
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024/7/11
Y1 - 2024/7/11
N2 - Sleep apnea (SA) is one of the most prevalent sleep-related problems, impacting more than 100 million people worldwide. A full-night Polysomnography (PSG) is an effective SA diagnosis strategy. However, it requires multiple wearable devices and the patient staying overnight to collect the findings, rendering it both a time-consuming and costly option. Research attempts to develop non-invasive, sensor-based, or automated solutions for diagnosing SA are also made in recent years. In this study, we analyzed a total of 85 papers, shortlisted from an initial collection of 954 articles published in reputable scientific repositories, e.g., IEEE Xplore, PubMed Central (PMC), Springer, Elsevier etc., where each chosen study was thoroughly examined to determine its contribution and performance. A detailed analysis of data preprocessing, feature extraction and classification algorithm is also addressed. It is found that most of the studies are based on signal analysis for identifying sleep apnea, which yields results with substantial reliability, while contemporary research emphases have been on heart rate variability and pulse oximetry outcomes.
AB - Sleep apnea (SA) is one of the most prevalent sleep-related problems, impacting more than 100 million people worldwide. A full-night Polysomnography (PSG) is an effective SA diagnosis strategy. However, it requires multiple wearable devices and the patient staying overnight to collect the findings, rendering it both a time-consuming and costly option. Research attempts to develop non-invasive, sensor-based, or automated solutions for diagnosing SA are also made in recent years. In this study, we analyzed a total of 85 papers, shortlisted from an initial collection of 954 articles published in reputable scientific repositories, e.g., IEEE Xplore, PubMed Central (PMC), Springer, Elsevier etc., where each chosen study was thoroughly examined to determine its contribution and performance. A detailed analysis of data preprocessing, feature extraction and classification algorithm is also addressed. It is found that most of the studies are based on signal analysis for identifying sleep apnea, which yields results with substantial reliability, while contemporary research emphases have been on heart rate variability and pulse oximetry outcomes.
KW - cloud computing
KW - deep learning
KW - electrocardiogram (ECG)
KW - Internet of Things (IoT)
KW - machine learning
KW - pulse oximetry
KW - Sleep apnea
KW - smartphone
KW - wearable devices
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UR - http://www.scopus.com/inward/citedby.url?scp=85203869098&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3426928
DO - 10.1109/ACCESS.2024.3426928
M3 - Review article
AN - SCOPUS:85203869098
SN - 2169-3536
VL - 12
SP - 122959
EP - 122987
JO - IEEE Access
JF - IEEE Access
ER -