Journal of Medical Systems
Discrimination ability of individual measures used in sleep stages classification
Artificial Intelligence in Medicine
Automated detection of neonate EEG sleep stages
Computer Methods and Programs in Biomedicine
Non-linear analysis of EEG signals at various sleep stages
Computer Methods and Programs in Biomedicine
Multivariate analysis of full-term neonatal polysomnographic data
IEEE Transactions on Information Technology in Biomedicine
Genetic fuzzy classifier for sleep stage identification
Computers in Biology and Medicine
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Self-evaluated automatic classifier as a decision-support tool for sleep/wake staging
Computers in Biology and Medicine
Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates
Journal of Medical Systems
Test-retest reliability and feature selection in physiological time series classification
Computer Methods and Programs in Biomedicine
Optimal channel selection for analysis of EEG-sleep patterns of neonates
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
An ensemble system for automatic sleep stage classification using single channel EEG signal
Computers in Biology and Medicine
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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 neural activity acquired using EEG. To calculate the instantaneous frequency of IMFs, an algorithm was developed using the Generalized Zero Crossing method. From the resulting frequencies, two different novel features were generated: the median instantaneous frequencies and the number of instantaneous frequency changes during a 30s segment for seven IMFs. The sleep stage classification for the daytime sleep of 20 healthy babies was determined using the Support Vector Machine classification algorithm. The results were evaluated using the cross-validation method to achieve an approximately 90% accuracy and with new examinee data to achieve 80% average accuracy of classification. The obtained results were higher than the human experts' agreement and were statistically significant, which positioned the method, based on the proposed features, as an efficient procedure for automatic sleep stage classification. The uniqueness of this study arises from newly proposed features of the time-frequency domain, which bind characteristics of the sleep signals to the oscillation modes of brain activity, reflecting the physical characteristics of sleep, and thus have the potential to highlight the congruency of twin pairs with potential implications for the genetic determination of sleep.