MULTIFRACTIONAL PROPERTY ANALYSIS OF HUMAN SLEEP EEG SIGNALS

2012 ◽  
Vol 22 (04) ◽  
pp. 1250080 ◽  
Author(s):  
HU SHENG ◽  
YANGQUAN CHEN ◽  
TIANSHUANG QIU
Keyword(s):  
Hurst Exponent ◽  
Dynamic Properties ◽  
Dynamic Assessment ◽  
Sleep Eeg ◽  
Dynamical Analysis ◽  
Sleep Stages ◽  
Hölder Exponent ◽  
Eeg Signals ◽  
Analysis Techniques ◽  
Human Sleep

Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, nonstationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis techniques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear analysis techniques. Among these techniques, the Hurst exponent estimation was widely used to characterize the fractional or scaling property of the EEG signals. However, the constant Hurst exponent H cannot capture the detailed information of dynamic EEG signals. In this research, the multifractional property of the normal human sleep EEG signals is investigated and characterized using local Hölder exponent H(t). The comparison of the analysis results for human sleep EEG signals in different stages using constant Hurst exponent H and the local Hölder exponent H(t) are summarized with tables and figures in the paper. The results of the analysis show that local Hölder exponent provides a novel and valid tool for dynamic assessment of brain activities in different sleep stages.

Multifractional Property Analysis of Human Sleep EEG Signals

2011 ◽  
Author(s):  
Hu Sheng ◽  
YangQuan Chen
Keyword(s):  
Hurst Exponent ◽  
Dynamic Properties ◽  
Dynamic Assessment ◽  
Sleep Eeg ◽  
Dynamical Analysis ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Nonlinear Dynamical ◽  
Analysis Techniques ◽  
Human Sleep

Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, non-stationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis techniques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear analysis techniques. Among these techniques, the Hurst exponent estimation was widely used to characterize the fractional or scaling property of the EEG signals. However, the constant Hurst exponent H cannot capture the the detailed information of dynamic EEG signals. In this research, the multifractional property of the normal human sleep EEG signals is investigated and characterized using local Holder exponent H(t). The comparison of the analysis results for human sleep EEG signals in different stages using constant Hurst exponent H and the local Ho¨lder exponent H(t) are summarized with tables and figures in the paper. The analysis results show that local Ho¨lder exponent provides a novel and valid tool for dynamic assessment of brain activities in different sleep stages.

Signal processing techniques applied to human sleep EEG signals—A review

2014 ◽  
Vol 10 ◽  
pp. 21-33 ◽  
Author(s):  
Shayan Motamedi-Fakhr ◽  
Mohamed Moshrefi-Torbati ◽  
Martyn Hill ◽  
Catherine M. Hill ◽  
Paul R. White
Keyword(s):  
Signal Processing ◽  
Sleep Eeg ◽  
Eeg Signals ◽  
Human Sleep ◽  
Processing Techniques ◽  
Signal Processing Techniques

Development of Automatic Scoring System for Sleep EEG Using Fuzzy Logic

1993 ◽  
Vol 5 (3) ◽  
pp. 204-208 ◽  
Author(s):  
Yoichi Tsuji ◽  
◽  
Takefumi Usui ◽  
Yasuhisa Sato ◽  
Kazuyuki Nagasawa
Keyword(s):  
Fuzzy Logic ◽  
Scoring System ◽  
Automatic Processing ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Wave Form ◽  
Computer Technique ◽  
Human Sleep ◽  
Automatic Scoring

Determination of the EEG sleep stages by clinicians is a time consuming task, because of a great amount of record obtained during one night. Therefore, an effective computer technique for an automatic processing EEG is strongly desired. We tried to make an automatic scoring system for human sleep EEG stages by a computer. The specific EEG wave form (e.g. delta, theta, alpha or spindle wave) was detected by means of a specific algorithm using a Fuzzy logic and an ""if...then..."" procedure. The scoring results by this system and clinicians agreed with each epoch more than 72%. This system is an available system for the clinical sleep EEG.

scholarly journals Sleep as a random walk - A superstatistical analysis of EEG data across sleep stages

2021 ◽  
Author(s):  
Claus Metzner ◽  
Achim Schilling ◽  
Maximilian Traxdorf ◽  
Holger Schulze ◽  
Patrick Krauss
Keyword(s):  
Sleep Stage ◽  
Muscular Activity ◽  
Temporal Correlation ◽  
Time Dependent ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Human Sleep ◽  
Eeg Data ◽  
Different Levels ◽  
Temporal Correlation Function

In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a superstatistical analysis by computing 'hyper-parameters', such as the standard deviation, kurtosis and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than subdivided process that controls human sleep.

scholarly journals Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Claus Metzner ◽  
Achim Schilling ◽  
Maximilian Traxdorf ◽  
Holger Schulze ◽  
Patrick Krauss
Keyword(s):  
Statistical Analysis ◽  
Sleep Stage ◽  
Quantitative Measure ◽  
Muscular Activity ◽  
Temporal Correlation ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Human Sleep ◽  
Eeg Data ◽  
Data Clusters

AbstractIn clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces.

scholarly journals Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach

2009 ◽  
Vol 02 (05) ◽  
pp. 294-303 ◽  
Author(s):  
Venkatakrishnan Perumalsamy ◽  
Sangeetha Sankaranarayanan ◽  
Sukanesh Rajamony
Keyword(s):  
Surrogate Data ◽  
Sleep Eeg ◽  
Ar Model ◽  
Sleep Spindles ◽  
Eeg Signals ◽  
Human Sleep

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Sarun Paisarnsrisomsuk ◽  
Carolina Ruiz ◽  
Sergio A. Alvarez
Keyword(s):  
Input Data ◽  
Single Channel ◽  
Sleep Stage ◽  
Quantitative Measure ◽  
Classification Performance ◽  
Sleep Stages ◽  
Layer Depth ◽  
Human Sleep ◽  
Stage Classification ◽  
Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

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