Detecting slow wave sleep using a single EEG signal channel

Detecting slow wave sleep using a single EEG signal channel.

BACKGROUND In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. NEW METHOD The proposed approach distinguishes itself from previous automat...

Detecting Slow Wave Sleep via One or Two Channels of EEG/EOG Signals

This work develops a number of automatic slow wave sleep (SWS) detection methods that employ only one or two channels of EOG/EEG signals. In addition to the reduction of signal channels, a distinct feature of the proposed approach is the introduction of a new feature set that can make the proposed approach insensitive to interpersonal differences of the physiological signals. The tested subject...

Sleep Stages Classification Using Neural Network with Single Channel EEG

The usual method for sleep stages classification is visual inspection method by sleep specialist. It uses eight EEG channels (O1, O2, T3, T4, C3, C4, Fp1 and Fp2), EOG and also EMG for sleep analysis. This method consumes more time (hours) for sleep stages classification. Some brain disorders like narcolepsy (excessive day time sleepiness) requires real-time monitoring of sleep states which is ...

Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal

This study presents a novel approach for the electroencephalogram (EEG) signal quantification in which the empirical mode decomposition method, a time-frequency method designated for nonlinear and non-stationary signals, decomposes the EEG signal into intrinsic mode functions (IMF) with corresponding frequency ranges that characterize the appropriate oscillatory modes embedded in the brain neur...

Continuous Unsupervised Sleep Staging Based on a Single EEG Signal