Understanding the neural mechanism of sleep using wavelets and multifractal techniques

Dynamical properties of large ensembles of neurons in the brain during sleep are found to be highly complex; therefore, nonlinear methods have found application in the analysis of random looking EEG signals. Here we have addressed the issue of the transition process from NREM to REM and vice versa using wavelet based multifractal formalism in healthy humans. We have used the technique based on wavelet transform modulus maxima (WTMM) to detect the irregularity in the structural pattern of sleep. The filtered sleep EEG data has been subjected to WTMM, the Singularity spectrum and the Hurst exponent has been computed using the Wavelab 8.5 toolbox. The results show graphically increasing bifurcations during the Non-REM to REM transitions. The narrow width of the multifractal spectrum indicate the REM state which further indicate the presence of many autonomous zones in the REM process. In Non-REM there are fewer bifurcations and the bandwidth of the multifractal spectrum is broad indicating the presence of a single large source contributing to the Non-REM process. The Hurst exponent for the REM sleep is found to be lower than the Hurst exponent for the Non-REM sleep, which can be used as an indicator of the transitions. Our findings show that the sleep transitions may be attributed to an increasing level of bifurcations and collapses that happen with an intermittent drive or force from the pontine and the brain stem structures.