Table of Contents Author Guidelines Submit a Manuscript
Neural Plasticity
Volume 2016, Article ID 6783812, 19 pages
http://dx.doi.org/10.1155/2016/6783812
Review Article

Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods

1GIGA In Silico Medicine, CRC (B30), University of Liège, 4000 Liège, Belgium
2Department of Electrical Engineering and Computer Science (B28), 4000 Liège, Belgium
3GIGA CRC In Vivo Imaging, CRC (B30), University of Liège, 4000 Liège, Belgium
4Department of Neurology, University of Liège Hospital (B35), 4000 Liège, Belgium
5Walloon Excellence in Life Sciences and Biotechnology (WELBIO), 1300 Wavre, Belgium

Received 8 February 2016; Accepted 27 April 2016

Academic Editor: Igor Timofeev

Copyright © 2016 Dorothée Coppieters ’t Wallant et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.