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Summary: In this research study we have developed a clustering‐based automatic sleep spindle detection method that was evaluated on two different databases. The databases consisted of 20 all‐night polysomnograph recordings. Past detection methods have been based on subject‐independent and some subject‐dependent parameters, such as fixed or variable thresholds to identify spindles. Using a multivariate Gaussian mixture model clustering technique, our algorithm was developed to use only subject‐specific parameters to detect spindles. We have obtained an overall sensitivity range (65.1–74.1%) at a (59.55–119.7%) false positive proportion. [ABSTRACT FROM AUTHOR]

Sleep spindle detection using modern signal processing techniques such as the Short-Time Fourier Transform and Wavelet Analysis are common research methods. These methods are computationally intensive, especially when analysing data from overnight sleep recordings. The authors of this paper propose an alternative using pre-designed IIR filters and a multivariate Gaussian Mixture Model. Features extracted with IIR filters are clustered using a Gaussian Mixture Model without the use of any subject independent thresholds. The Algorithm was tested on a database consisting of overnight sleep PSG of 5 subjects and an online public spindles database consisting of six 30 minute sleep excerpts. An overall sensitivity of 57% and a specificity of 98.24% was achieved in the overnight database group and a sensitivity of 65.19% at a 16.9% False Positive proportion for the 6 sleep excerpts.

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.

The characterisation of functional interdependencies of the autonomic nervous system (ANS) stands an evergrowing interest to unveil electroencephalographic (EEG) and Heart Rate Variability (HRV) interactions. This paper presents a biosignal processing approach as a supportive computational resource in the estimation of sleep dynamics. The application of linear, non-linear methods and statistical tests upon 10 overnight polysomnographic (PSG) recordings, allowed the computation of wavelet coherence and phase locking values, in order to identify discerning features amongst the clinical healthy subjects. Our findings showed that neuronal oscillations θ, α and σ interact with cardiac power bands at mid-to-high rank of coherence and phase locking, particularly during NREM sleep stages.