Stage-independent, single lead EEG sleep spindle detection using the continuous wavelet transform and local weighted smoothing.

Athanasios Tsanas,Gari D Clifford

doi:10.3389/fnhum.2015.00181

Abstract

Sleep spindles are critical in characterizing sleep and have been associated with cognitive function and pathophysiological assessment. Typically, their detection relies on the subjective and time-consuming visual examination of electroencephalogram (EEG) signal(s) by experts, and has led to large inter-rater variability as a result of poor definition of sleep spindle characteristics. Hitherto, many algorithmic spindle detectors inherently make signal stationarity assumptions (e.g., Fourier transform-based approaches) which are inappropriate for EEG signals, and frequently rely on additional information which may not be readily available in many practical settings (e.g., more than one EEG channels, or prior hypnogram assessment). This study proposes a novel signal processing methodology relying solely on a single EEG channel, and provides objective, accurate means toward probabilistically assessing the presence of sleep spindles in EEG signals. We use the intuitively appealing continuous wavelet transform (CWT) with a Morlet basis function, identifying regions of interest where the power of the CWT coefficients corresponding to the frequencies of spindles (11–16 Hz) is large. The potential for assessing the signal segment as a spindle is refined using local weighted smoothing techniques. We evaluate our findings on two databases: the MASS database comprising 19 healthy controls and the DREAMS sleep spindle database comprising eight participants diagnosed with various sleep pathologies. We demonstrate that we can replicate the experts' sleep spindles assessment accurately in both databases (MASS database: sensitivity: 84%, specificity: 90%, false discovery rate 83%, DREAMS database: sensitivity: 76%, specificity: 92%, false discovery rate: 67%), outperforming six competing automatic sleep spindle detection algorithms in terms of correctly replicating the experts' assessment of detected spindles.

Highlights

Sleep spindles are characteristic oscillatory patterns of brain activity which can be visually detected in human electroencephalography (EEG) signals
This study extends the methodology of recent approaches using the Continuous Wavelet Transform (CWT) with Morlet basis functions (Sitnikova et al, 2009; Wamsley et al, 2012)
Evaluation of the Spindle Detection Algorithms on the DREAMS Sleep Spindles Database Tables 1–3 summarize the performance of the sleep spindle detection algorithms used in this study for each of the eight signals

Summary

Introduction

Sleep spindles are characteristic oscillatory patterns of brain activity which can be visually detected in human electroencephalography (EEG) signals. The presence of sleep spindles is one of the hallmarks for determining stage 2 (S2) in the hypnogram, which provides an overall representation of sleep structure successively assigning short signal segments (known as epochs, usually of 30 s duration) to one of five sleep stages (Iber et al, 2007). They have been associated with various higher cognitive processes in particular memory (Tamminen et al, 2010), and learning performance (Schmidt et al, 2006) and skill performance (Astill et al, 2015). There is a growing body of research literature highlighting their potential as biomarkers: a number of studies have reported clinically significant differences in spindle characteristics for a range of neurological disorders (Ferrarelli et al, 2007; Wamsley et al, 2012; Christensen et al, 2014)

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Conclusion