Advanced sleep spindle identification with neural networks

Scientific Reports, vol. 12, pp. 7686

Abstract

Sleep spindles are neurophysiological phenomena that appear to be linked to memory formation and other functions of the central nervous system, and that can be observed in electroencephalographic recordings (EEG) during sleep. Manually identified spindle annotations in EEG recordings suffer from substantial intra- and inter-rater variability, even if raters have been highly trained, which reduces the reliability of spindle measures as a research and diagnostic tool. The Massive Online Data Annotation (MODA) project has recently addressed this problem by forming a consensus from multiple such rating experts, thus providing a corpus of spindle annotations of enhanced quality. Based on this dataset, we present a U-Net-type deep neural network model to automatically detect sleep spindles. Our model's performance exceeds that of the state-of-the-art detector and of most experts in the MODA dataset. We observed improved detection accuracy in subjects of all ages, including older individuals whose spindles are particularly challenging to detect reliably. Our results underline the potential of automated methods to do repetitive cumbersome tasks with super-human performance.

Authors 5

1. Lars Kaulen Aachen Department of Medical Engineering and Technomathematics

   FH Aachen

   Affiliation as printed

   Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, 52428, Jülich, Germany

2. Justus T. C. Schwabedal
   Affiliation as printed

   Independent researcher, Lessingstraße 65, 53113, Bonn, Germany

3. Jules Schneider

   University Hospital Carl Gustav Carus · Technische Universität Dresden

   Affiliation as printed

   Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307, Dresden, Germany

4. Philipp S. Ritter

   University Hospital Carl Gustav Carus · Technische Universität Dresden

   Affiliation as printed

   Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307, Dresden, Germany

5. Stephan Bialonski corresponding Aachen Department of Medical Engineering and Technomathematics Institute for Data-Driven Technologies

   FH Aachen

   Affiliation as printed

   Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, 52428, Jülich, Germany. bialonski@fh-aachen.de

   Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, 52428, Jülich, Germany. bialonski@fh-aachen.de

   Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, 52428, Jülich, Germany