An ensemble system for automatic sleep stage classification using single channel EEG signal

Authors:
B. Koley;D. Dey
Affiliations:
Department of Instrumentation Engineering, Dr. B.C. Roy Engineering College, Durgapur 713206, West Bengal, India;Department of Electrical Engineering, Jadavpur University, Kolkata, West Bengal, India
Venue:
Computers in Biology and Medicine
Year:
2012

Citing 10
Cited 2

Approach to an irregular time series on the basis of the fractal theory

Physica D
The nature of statistical learning theory

The nature of statistical learning theory
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Discrimination ability of individual measures used in sleep stages classification

Artificial Intelligence in Medicine
Non-linear analysis of EEG signals at various sleep stages

Computer Methods and Programs in Biomedicine
Genetic fuzzy classifier for sleep stage identification

Computers in Biology and Medicine
Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting

Expert Systems with Applications: An International Journal
Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging

Computers in Biology and Medicine
Automatic sleep scoring: A search for an optimal combination of measures

Artificial Intelligence in Medicine
A reliable probabilistic sleep stager based on a single EEG signal

Artificial Intelligence in Medicine

Automatic classification of sleep stages based on the time-frequency image of EEG signals

Computer Methods and Programs in Biomedicine
Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal

Computers in Biology and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The present work aims at automatic identification of various sleep stages like, sleep stages 1, 2, slow wave sleep (sleep stages 3 and 4), REM sleep and wakefulness from single channel EEG signal. Automatic scoring of sleep stages was performed with the help of pattern recognition technique which involves feature extraction, selection and finally classification. Total 39 numbers of features from time domain, frequency domain and from non-linear analysis were extracted. After extraction of features, SVM based recursive feature elimination (RFE) technique was used to find the optimum number of feature subset which can provide significant classification performance with reduced number of features for the five different sleep stages. Finally for classification, binary SVMs were combined with one-against-all (OAA) strategy. Careful extraction and selection of optimum feature subset helped to reduce the classification error to 8.9% for training dataset, validated by k-fold cross-validation (CV) technique and 10.61% in the case of independent testing dataset. Agreement of the estimated sleep stages with those obtained by expert scoring for all sleep stages of training dataset was 0.877 and for independent testing dataset it was 0.8572. The proposed ensemble SVM-based method could be used as an efficient and cost-effective method for sleep staging with the advantage of reducing stress and burden imposed on subjects.