Automated Sleep Spindle detection using novel EEG features and mixture models

Research in automated Sleep Spindle detection has been highly explored in the past few years. Although a number of automated techniques were developed, many of them were based on using fixed parameters or thresholds which do not consider subject specific differences. In this research study, we introduce a novel method of sleep spindle detection using Gaussian Mixture Models with no fixed parameters or thresholds. The algorithm was tested on an online public spindles database consisting of six 30 minute sleep excerpts extracted from whole night recordings of 6 subjects. The results obtained were better when compared with other methods. We obtained an overall sensitivity of 74.9% at a 28% False Positive proportion.