Sleep Stages Clustering Using Time and Spectral Features of EEG Signals

Rodríguez-Sotelo, José Luis; Osorio-Forero, Alejandro; Jiménez-Rodríguez, Alejandro; Mejía, Francia Restrepo de; Peluffo-Ordóñez, Diego H.; Serrano, Joaquim Rigola

doi:10.1007/978-3-319-59740-9_44

Cited by 1 publication

(1 citation statement)

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“…It was used in an investigation of sleep EEG for automated k-complex detection [67], [68], for sleep stages and apnea in children [69], diagnosis of OSA [70], [71] and in a study of drug effects [72]. Back-propagation trained ANN has been used for REM, sleep spindles, and waking state in their automatic detection [71].…”

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%

A Systematic Review of Literature on Automated Sleep Scoring

Alsolai

Qureshi

Iqbal³

et al. 2022

IEEE Access

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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.

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