Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Optimal and Adaptive Signal Processing
Optimal and Adaptive Signal Processing
A method for the automatic analysis of the sleep macrostructure in continuum
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
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Detection and quantification of sleep arousals is an important issue, as the frequent arousals are known to reduce the quality of sleep and cause daytime sleepiness. In typical sleep staging, electroencephalograph (EEG) is the core signal and based on the visual inspection of the frequency content of EEG, non-rapid eye movement sleep is staged into four somewhat rough categories. In this study, we aimed at developing a continuous marker based on a more rigorous spectral analysis of EEG to measure or quantify the depth of sleep. In order to develop such a marker, we obtained the time-frequency map of two EEG channels around sleep arousals and identified the frequency bands that show the most change during arousals. We then evaluated classification performance of the potential signals for representing the depth of sleep, using receiver operating characteristic analysis. Our comparisons based on the area under the curve values revealed that the sum of absolute powers in alpha and beta bands is a good continuous marker to represent the depth of sleep. Higher values of this marker indicate low-quality sleep and vice versa. We believe that use of this marker will lead to a better quantification of sleep quality.