OBJECTIVE: Network analysis of electroencephalograph (EEG) signals to study the effect of fatigue and sleep deprivation in human drivers and its validation using blood biochemical parameters. METHODS: We present a new method of detection of human fatigue and sleepiness by studying the variation of functional interdependencies among EEG signals from various channels. An experiment has been designed to induce fatigue in 12 subjects through several stages over 36 h of sleep deprivation. The functional interdependency among the signals has been computed using synchronisation likelihood (SL), which measures the dynamical (both linear and non-linear) interdependency between two or more non-stationary time series. A network structure has been generated based on the likelihood values and is characterised by a number of standard network-characterising parameters at each stage. Finally, the trends in the network parameters have been validated using biochemical analysis of three blood parameters: glucose, blood urea and creatinine. RESULTS: An increasing trend in the degree of connectivity and clustering coefficient and a decreasing trend in the characteristic path length have been observed in some bands of signals at successive stages of the experiment. CONCLUSIONS: Synchronisation of specific bands of the EEG signals from different cortical areas has been observed along with variation in network parameters at increased levels of fatigue and sleep deprivation. SIGNIFICANCE: The results indicate that the network parameters may be used to detect and quantify the level of fatigue and sleepiness.
OBJECTIVE: Network analysis of electroencephalograph (EEG) signals to study the effect of fatigue and sleep deprivation in human drivers and its validation using blood biochemical parameters. METHODS: We present a new method of detection of humanfatigue and sleepiness by studying the variation of functional interdependencies among EEG signals from various channels. An experiment has been designed to induce fatigue in 12 subjects through several stages over 36 h of sleep deprivation. The functional interdependency among the signals has been computed using synchronisation likelihood (SL), which measures the dynamical (both linear and non-linear) interdependency between two or more non-stationary time series. A network structure has been generated based on the likelihood values and is characterised by a number of standard network-characterising parameters at each stage. Finally, the trends in the network parameters have been validated using biochemical analysis of three blood parameters: glucose, blood urea and creatinine. RESULTS: An increasing trend in the degree of connectivity and clustering coefficient and a decreasing trend in the characteristic path length have been observed in some bands of signals at successive stages of the experiment. CONCLUSIONS: Synchronisation of specific bands of the EEG signals from different cortical areas has been observed along with variation in network parameters at increased levels of fatigue and sleep deprivation. SIGNIFICANCE: The results indicate that the network parameters may be used to detect and quantify the level of fatigue and sleepiness.
Authors: Catherine J Chu; Mark A Kramer; Jay Pathmanathan; Matt T Bianchi; M Brandon Westover; Lauren Wizon; Sydney S Cash Journal: J Neurosci Date: 2012-02-22 Impact factor: 6.167
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