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A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and

Ahnaf Rashik Hassan¹, Mohammed Imamul Hassan Bhuiyan¹

¹Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.

Journal of Neuroscience Methods

|July 27, 2016

Summary

This study introduces an automated sleep scoring method using tunable-Q factor wavelet transform (TQWT) and random forest classification. The approach significantly improves sleep stage classification accuracy, aiding faster diagnosis and research.

Keywords:

EEG Random forest Sleep stage classification Spectral features Tunable-Q factor wavelet transform

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Area of Science:

Biomedical Engineering
Signal Processing
Machine Learning

Background:

Manual sleep scoring is time-consuming, labor-intensive, and prone to errors.
Automated sleep staging is crucial for efficient and accurate diagnosis of sleep disorders.

Purpose of the Study:

To develop and validate an automated sleep scoring system.
To leverage spectral features from TQWT for improved sleep stage classification.

Main Methods:

Sleep-EEG signal segments were decomposed using tunable-Q factor wavelet transform (TQWT).
Spectral features were extracted from TQWT sub-bands.
Random forest classifier was employed for sleep stage classification.
Parameter optimization for TQWT and random forest was performed.

Main Results:

The proposed method achieved high classification accuracies, ranging from 90.38% to 97.50% on the Sleep-EDF dataset.
Statistical validation using ANOVA and Fisher criteria confirmed the efficacy of the feature generation scheme.
Promising results were also observed on the DREAMS Subjects Data-set.

Conclusions:

TQWT-derived spectral features effectively discriminate between various sleep stages.
The automated scheme significantly outperforms existing methods in accuracy and Cohen's kappa coefficient.
This approach can reduce physician workload, accelerate diagnosis, and advance sleep research.