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A Systematic Review of Literature on Automated Sleep Scoring

Hadeel Alsolai (Författare), Shahnawaz Qureshi (Författare), Syed Muhammad Zeeshan Iqbal (Författare), Sirirut Vanichayobon (Författare), Lawrence Henesey (Författare)

URI http://urn.kb.se/resolve?urn=urn:nbn:se:bth-23557, URN urn:nbn:se:bth-23557, DOI 10.1109/access.2022.3194145, Identifikator http://bth.diva-portal.org/smash/get/diva2:1688615/FULLTEXT01 · url

Blekinge Tekniska Högskola Fakulteten för datavetenskaper (Utgivare)

utgivning

Institute of Electrical and Electronics Engineers (IEEE), 2022

är del av

IEEE Access

10, 79419-79443

Bidrag, Onlineresurs

Finns online:

http://urn.kb.se/resolve?urn=urn:nbn:se:bth-23557

Värdpublikation

ämne

Natural Sciences, Computer and Information Sciences, Computer Sciences, Naturvetenskap, Data- och informationsvetenskap, Datavetenskap (datalogi), Sleep, Feature extraction, Machine learning, Electroencephalography, StandardsSleep apnea, Deep learning, Artificial neural network, automatic sleep scoring system, big data, feature extraction, inter-rater variability, machine learning, sleep stages

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Sammanfattning

Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

Detaljer

Medverkan och funktion

Hadeel Alsolai (Författare), Shahnawaz Qureshi (Författare), Syed Muhammad Zeeshan Iqbal (Författare), Sirirut Vanichayobon (Författare), Lawrence Henesey (Författare)

Medverkan och funktion

Blekinge Tekniska Högskola Fakulteten för datavetenskaper (Utgivare)

Identifikator

URI http://urn.kb.se/resolve?urn=urn:nbn:se:bth-23557, URN urn:nbn:se:bth-23557, DOI 10.1109/access.2022.3194145, Identifikator http://bth.diva-portal.org/smash/get/diva2:1688615/FULLTEXT01 · url