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Stages of Sleep

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Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
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Multi-channel EEG-based sleep staging using brain functional connectivity and domain adaptation.

Wenhao Yuan¹, Wentao Xiang², Kaiyue Si¹

¹Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China.

Physiological Measurement

|October 12, 2023

Summary

This study introduces brain functional connectivity features for sleep stage recognition using electroencephalography (EEG) signals. Domain adaptation methods significantly improve accuracy in subject-independent sleep staging.

Keywords:

domain adaptation energy ratio functional connectivity multi-channel EEG signal sleep stage recognition

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Sleep stage recognition is crucial for diagnosing sleep disorders and assessing health.
Existing methods often overlook brain connectivity and signal non-stationarity.
Electroencephalography (EEG) is a key tool for monitoring brain activity during sleep.

Purpose of the Study:

To develop a novel brain functional connectivity approach for multi-channel EEG sleep staging.
To evaluate the efficacy of synchronization likelihood (SL), wavelet-based correlation (WC), and energy ratio (ER) features.
To assess the impact of domain adaptation (DA) on subject-independent sleep staging accuracy.

Main Methods:

Utilized six-channel EEG data from twenty subjects in the ISRUC-SLEEP dataset.
Extracted frequency-domain features: SL and WC across four bands, ER across six bands.
Employed Gaussian support vector machine (SVM) for five-class sleep stage classification.
Validated performance using ten-fold cross-validation (subject-dependent) and leave-one-subject-out (subject-independent) methods.
Applied five domain adaptation techniques to address inter-subject variability.

Main Results:

Subject-dependent classification accuracy reached 83.97% ± 1.04% with fused SL, WC, and ER features.
Subject-independent accuracy was lower (57.44%) due to individual EEG differences.
Four DA methods significantly improved subject-independent accuracy by 1.89%-5.22%.

Conclusions:

Brain functional connectivity features effectively capture inter-regional brain correlations for sleep staging.
Domain adaptation methods are vital for enhancing sleep staging algorithm performance by mitigating individual EEG variations.
The proposed approach offers a promising direction for more robust and accurate automated sleep analysis.