A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Published Date: Jan 31, 2018

Abstract

OBJECTIVE: Limitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electro-oculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels have long been recognized. Manual staging is resource intensive and time consuming, and thus considerable effort must be spent to ensure inter-rater reliability. As a result, there is a great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC).

Authors

  • Stavros I Dimitriadis
    Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom. Electronic address: stidimitriadis@gmail.com.
  • Christos Salis
    University of Western Macedonia, Department of Informatics and Telecommunications Engineering, Kozani, Greece.
  • David Linden
    Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.

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