Journal ArticleDOI
Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal
Guohun Zhu,Yan Li,Peng Paul Wen +2 more
TLDR
It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDS of the HVGs are just the reverse.Abstract:
The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P (k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of P (k) from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.read more
Citations
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Journal ArticleDOI
Complex network approaches to nonlinear time series analysis
Yong Zou,Reik V. Donner,Norbert Marwan,Jonathan F. Donges,Jonathan F. Donges,Jürgen Kurths,Jürgen Kurths,Jürgen Kurths +7 more
TL;DR: An in-depth review of existing approaches of time series networks, covering their methodological foundations, interpretation and practical considerations with an emphasis on recent developments, and emphasizes which fundamental new insights complex network approaches bring into the field of nonlinear time series analysis.
Journal ArticleDOI
A convolutional neural network for sleep stage scoring from raw single-channel EEG
TL;DR: A deep convolutional neural network is introduced on raw EEG samples for supervised learning of 5-class sleep stage prediction and a method for visualizing class-wise patterns learned by the network is presented.
Journal ArticleDOI
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation
TL;DR: A novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals is presented.
Journal ArticleDOI
Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating
TL;DR: A single-channel EEG based method for sleep staging using recently introduced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Bootstrap Aggregating (Bagging) is proposed and gives high detection accuracy for sleep stages S1 and REM.
Journal ArticleDOI
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
TL;DR: In this paper, an attention-based deep learning architecture called AttnSleep was proposed to classify sleep stages using single-channel EEG signals, which leverages a multi-head attention mechanism to capture the temporal dependencies among the extracted features.
References
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Journal ArticleDOI
The measurement of observer agreement for categorical data
J. R. Landis,Gary G. Koch +1 more
TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Journal ArticleDOI
A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects.
TL;DR: Techniques of recording, scoring, and doubtful records are carefully considered, and Recommendations for abbreviations, types of pictorial representation, order of polygraphic tracings are suggested.