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

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology
Stavros I DimitriadisDavid Linden

Abstract

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). In this paper, we present a single-EEG-sensor ASSC technique based on the dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5 s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database with repeat recordings from 20 healthy young adults. We validate our methodology in a second sleep dataset. We achieved very high classification sensitivity, specificity and accuracy of 96.2 ± 2.2%, 94.2 ± 2.3%, and 94.4 ± 2.2% across 20 folds, respectively, and also a high mean F1 score (92%, range 90-94%) when a multi-class Naive Bayes classifier was applied. High classification performance has been ...Continue Reading

Citations

Nov 21, 2018·Brain Sciences·Rodrigo RameleJuan Miguel Santos
Nov 8, 2020·Journal of Neuroscience Methods·Rui YanFengyu Cong
Apr 14, 2021·Journal of Neural Engineering·Stavros I DimitriadisDimitris Liparas
Aug 8, 2021·Journal of Clinical Medicine·Ramón Martín-BrufauCristina Nombela
Apr 30, 2021··Ana Leonor RiveraAlejandro Hernández-Cano

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