Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures

Fell, Juergen; Röschke, Joachim; Mann, Klaus; Schäffner, Cornelius

doi:10.1016/0013-4694(96)95636-9

Cited by 251 publications

(113 citation statements)

References 34 publications

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“…This entropy measure has already been used in sleep EEG signal processing, yielding high values in wakefulness and REM sleep stages, and low values in N3 stages [28]. Shannon entropy was calculated for each channel independently (features ψ 9 and ψ 10 ).…”

Section: Shannon Entropymentioning

confidence: 99%

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

100

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Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-α algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.

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Section: Shannon Entropymentioning

confidence: 99%

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Rodríguez-Sotelo

Osorio-Forero

Jiménez-Rodríguez

et al. 2014

Entropy

100

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“…Besides alpha power, 9 different EEG measures were evaluated, including 7 spectral (frequency based) measures plus the nonlinear measures correlation dimension D2 and Lyapunov-exponent L1 (see e.g., Fell et al, 1996). The calculation of all spectral measures was based on FFTs using cosine windowing in the time domain (window length = 5.12 s).…”

mentioning

confidence: 99%

Covariation of Spectral and Nonlinear Eeg Measures With Alpha Biofeedback

Fell

Elfadil

Klaver

et al. 2002

International Journal of Neuroscience

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“…Spectral entropy is evaluated using the normalized Shannon entropy, which quantifies the spectral complexity of the time series [16,17]. Spectral entropy uses the power spectrum of the signal to estimate the regularity of time series; its amplitude components are used to compute the probabilities in entropy computation.…”

Section: Spectral Entropymentioning

confidence: 99%

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

Zhang

Min

2017

Applied Sciences

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Driver fatigue has become one of the major causes of traffic accidents, and is a complicated physiological process. However, there is no effective method to detect driving fatigue. Electroencephalography (EEG) signals are complex, unstable, and non-linear; non-linear analysis methods, such as entropy, maybe more appropriate. This study evaluates a combined entropy-based processing method of EEG data to detect driver fatigue. In this paper, 12 subjects were selected to take part in an experiment, obeying driving training in a virtual environment under the instruction of the operator. Four types of enthrones (spectrum entropy, approximate entropy, sample entropy and fuzzy entropy) were used to extract features for the purpose of driver fatigue detection. Electrode selection process and a support vector machine (SVM) classification algorithm were also proposed. The average recognition accuracy was 98.75%. Retrospective analysis of the EEG showed that the extracted features from electrodes T5, TP7, TP8 and FP1 may yield better performance. SVM classification algorithm using radial basis function as kernel function obtained better results. A combined entropy-based method demonstrates good classification performance for studying driver fatigue detection.

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Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures

Cited by 251 publications

References 34 publications

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

Covariation of Spectral and Nonlinear Eeg Measures With Alpha Biofeedback

Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features

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