Research on Best Practices for EEG Analysis in Sleep Stage Scoring

This paper outlines the best practices in utilizing the electroencephalogram (EEG) signals for scoring sleep stages, deliberates on their effectiveness in practical applications, and examines the impacts of various feature selection approaches. The research findings indicate optimal outcomes when EEG signals recorded from electrodes Fp2-F4 are used. This is achieved by i) implementing fourth-order Butterworth filters and isolating the five EEG rhythm bands (alpha, beta, gamma, delta, and theta), and ii) employing the spectra and spectrogram of the extracted EEG bands as input for a support vector machine (SVM) for automatic sleep stage scoring. It separates the sleep states based on the energy estimation and considering the maxim-minim distance for samples of duration 10 seconds, using a limited amount of data for training, in order to minimize the volume of samples which overlap and to avoid overfitting. Other electrodes can be further considered, the strongest received signal changing in time and among patients. It is important to highlight that this paper proves highly valuable for accelerating the classification of sleep stages, particularly when an enough amount of input data is available.