Literature review: Sleep stage classification based on EEG signals using artificial intelligence technique

Mishra, Satyam; Birok, Rajesh

doi:10.1201/9781003193838-44

Cited by 5 publications

(3 citation statements)

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“…Deep learning models learn to identify important features in the EEG signals and use these features to classify the sleep stages when provided with sufficient training examples [19] , [20] . The recent increase in available public datasets [21] enabled these methods to achieve state-of-the-art performance on sleep staging [22] .…”

Section: Introductionmentioning

confidence: 99%

Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring

et al. 2023

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Sleep is essential for physical and mental health. Polysomnography (PSG) procedures are labour-intensive and time-consuming, making diagnosing sleep disorders difficult. Automatic sleep staging using ML-based methods has been studied extensively, but frequently provides noisier predictions incompatible with typical manually annotated hypnograms. We propose an energy optimization method to improve the quality of hypnograms generated by automatic sleep staging procedures. The method evaluates the system's total energy based on conditional probabilities for each epoch's stage and employs an energy minimisation procedure. It can be used as a meta-optimisation layer over the sleep stage sequences generated by any classifier that generates prediction probabilities. Results show that it improves the accuracy of the predictions of state-ofthe-art Deep Learning models using two public datasets.

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Section: Introductionmentioning

confidence: 99%

Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring

et al. 2023

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Section: Introductionmentioning

confidence: 99%

“…The utilisation of machine learning (ML) techniques for sleep stage classification has been extensively examined in literature reviews [25,[28][29][30][31][32][33]. The systematic reviews in [25,29,31] focus on models of sleep stage classification based on single-channel EEG signals. The advantages of using single-channel EEG signals include convenience and ease of use, and they can be adapted for use in the patient's home using wearable sensors.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review

Almutairi,

Hassan,

Datta

2023

Applied Sciences

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Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research.

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