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- W2011220959 abstract "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 ." @default.
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- W2011220959 date "2014-01-01" @default.
- W2011220959 modified "2023-09-27" @default.
- W2011220959 title "A Measure to Detect Sleep Onset Using Statistical Analysis of Spike Rhythmicity" @default.
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- W2011220959 doi "https://doi.org/10.4018/ijbce.2014010103" @default.
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