A Measure to Detect Sleep Onset Using Statistical Analysis of Spike Rhythmicity


Electroencephalogram (EEG) signals derived from polysomnography recordings play an important role in assessing the physiological and behavioral changes during onset of sleep. This paper suggests a spike rhythmicity based feature for discriminating the wake and sleep state. The polysomnography recordings are segmented into 1 second EEG patterns to ensure stationarity of the signal and four windowing scheme overlaps (0%, 50%, 60% and 75%) of EEG pattern are introduced to study the influence of the pre-processing procedure. The application of spike rhythmicity feature helps to estimate the number of spikes from the given pattern with a threshold of 25%.Then non parametric statistical analysis using Wilcoxon signed rank test is introduced to evaluate the impact of statistical measures such as mean, standard deviation, p-value and box-plot analysis under various conditions.The statistical test shows significant difference between wake and sleep with p<0.005 for the applied feature, thus demonstrating the efficiency of simple thresholding in distinguishing sleep and wake stage.

Cite this paper

@inproceedings{Santamara2016AMT, title={A Measure to Detect Sleep Onset Using Statistical Analysis of Spike Rhythmicity}, author={Joan Santamar{\'i}a and Valentina Isetta and {\'A}lex Iranzo and Daniel Navajas and Ramon Farr{\'e}}, year={2016} }