Model-based Quantification of the Time- Varying Microstructure of Sleep Eeg Spindles: Possibility for Eeg-based Dementia Biomarkers

The time-varying microstructure of sleep EEG spindles may have clinical significance in dementia studies and can be quantified with a number of techniques. In this paper, the sleep spindle is modeled as an AM-FM signal in terms of six parameters, three quantifying the instantaneous envelope (IE) and three quantifying the instantaneous frequency (IF) of the spindle model. An application of such parameterization is proposed, in search of EEG-based biomarkers in dementia. The IE and IF waveforms of actual sleep spindles were estimated using the time-frequency technique of Complex Demodulation (CD). Sinusoidal curve-fitting using a matching pursuit (MP) approach was applied to the IE and IF waveforms, from which the six model parameters were subsequently estimated. Preliminary results indicate that the proposed parameterization may be promising, since it quantified specific differences in sleep spindle instantaneous frequency dynamics between spindles from dementia subjects and spindles from normal controls.