Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

By Khald Ali I. Aboalayon, Miad Faezipour, Wafaa S. Almuhammadi, Saeid Moslehpour. Entropy, 2016

354 citations recorded by the source corpus.

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/54743b953ed2508fdc5f381be6f7122d44dd88ff

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Citations in the corpus

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification (2018, background): “Automatic sleep stage classification [3], that performs as well as manual scoring, can help to”
Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (2017, background): “For a recent survey on the available literature refer to [3].”
Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI (2024, background): “…in seizure epilepsy classification (Boonyakitanont et al., 2020), acute stress detection (Sharma et al., 2022), sleep stage classification (Aboalayon et al., 2016), motor imagery recognition (Amin et al., 2019), abnormal identification (Roy et al., 2019), emotion analysis (Suhaimi et al.,…”
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, background): “(see [7] for a very extensive list of references).”
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging (2018, neutral): “Apart from a large body of hand-crafted features [29],”
Deep learning-based electroencephalography analysis: a systematic review (2019, background): “For example, in clinical settings, EEG is often used to study sleep patterns [1] or epilepsy [3].”