EOG and EMG: Two Important Switches in Automatic Sleep Stage Classification

Estrada, Edson; Nazeran, Homer; Barragan, Jaime; Burk, John R.; Lucas, E.A.; Behbehani, Khosrow

doi:10.1109/iembs.2006.260075

Cited by 31 publications

(19 citation statements)

References 3 publications

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“…2 illustrates both manual and automatic scoring obtained for the first 320 epochs of a single subject. Note that in most automatic sleep scoring systems both EMG and EOG are considered for differentiating the REM stage [18], [19]. However, in this study we have performed single channel (C3-A2) EEG analysis.…”

Section: A Resultsmentioning

confidence: 99%

“…Thus, the spectral power is utilized to compute the relevant frequency. The peak in the Fourier transform is needed to fall into the bandwidth of interest delta (1-4 Hz), theta (4-7.5 Hz), alpha (7.5-12 Hz), beta1 (16)(17)(18), and beta2 (18-40 Hz).…”

Section: B Constrained Ssamentioning

confidence: 99%

“…• The mean and standard deviation (SD) of different subbands including delta (up to 4 Hz), theta (4-7.5 Hz), alpha (7.5-12 Hz), lower beta or beta1 (16)(17)(18), and higher beta or beta2 (18-40 Hz).…”

Section: Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of awake, REM, and NREM from EEG via singular spectrum analysis

Mohammadi

Enshaeifar

Ghavami

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this study, a single-channel electroencephalography (EEG) analysis method has been proposed for automated 3-state-sleep classification to discriminate Awake, NREM (non-rapid eye movement) and REM (rapid eye movement). For this purpose, singular spectrum analysis (SSA) is applied to automatically extract four brain rhythms: delta, theta, alpha, and beta. These subbands are then used to generate the appropriate features for sleep classification using a multi class support vector machine (M-SVM). The proposed method provided 0.79 agreement between the manual and automatic scores.

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Section: A Resultsmentioning

confidence: 99%

Section: B Constrained Ssamentioning

confidence: 99%

See 1 more Smart Citation

Classification of awake, REM, and NREM from EEG via singular spectrum analysis

Mohammadi

Enshaeifar

Ghavami

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…Humans spend around one third of their life sleeping [1] [2]. Sleep is separated into six different stages: Wakefulness, Rapid Eye Movement (REM) sleep and Non-REM (NREM).…”

Section: A Backgroundmentioning

confidence: 99%

A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages

Aboalayon

Almuhammadi

Faezipour

2015

2015 Long Island Systems, Applications and Technology

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In recent years, the estimation of human sleep disorders from Electroencephalogram (EEG) signals have played an important role in developing automatic detection of sleep stages. A few methods exist in the market presently towards this aim. However, sleep physicians may not have full assurance and consideration in such methods due to concerns associated with systems accuracy, sensitivity and specificity. This paper presents a novel and efficient technique that can be implemented in a microcontroller device to identify sleep stages in an effort to assist physicians in the diagnosis and treatment of related sleep disorders by enhancing the accuracy of the developed algorithm using a single channel of EEG signals. First, the dataset of EEG signal is filtered and decomposed into delta, theta, alpha, beta and gamma subbands using Butterworth band-pass filters. Second, a set of sample statistical discriminating features are derived from each frequency band. Finally, sleep stages consisting of Wakefulness, Rapid Eye Movement (REM) and Non-Rapid Eye Movement (NREM) are classified using several supervised machine learning classifiers including multi-class Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), K-Nearest Neighbors (KNN) and Naive Bayes (NB). This paper combines REM with Stage 1 NREM due to data similarities. Performance is then compared based on single channel EEG signals that were obtained from 20 healthy subjects. The results show that the proposed technique using DT classifier efficiently achieves high accuracy of 97.30% in differentiating sleeps stages. Also, a comparison of our method with some recent available works in the literature reiterates the high classification accuracy performance.

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“…Physiological signals are characterized by dynamic processes with various interactions among several physiology systems, which contain a lot of information that can reflect the stage of a biological system [6]. Most sleep studies use all-night Polysomnographic (PSG) recordings, including electroencephalogram (EEG) [7,8], electrocardiogram(ECG) [9], and Electromyogram(EMG) [10], to monitor the stages of sleep. Compared with other physiological signals, non-invasive EEG recordings can most intuitively reflect changes in physiological systems and is widely used in sleep analysis [11].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

Li¹,

Tang²,

Liu³

et al. 2020

Preprint

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Background: Conventional manual sleep stage classiﬁcation is time-consuming and relies on the knowledge and experience of the specialists. The emergence of automatic sleep stage classiﬁcation greatly improves the classiﬁcation eﬃciency. The feature extraction in automatic sleep stage classiﬁcation is particularly important, which usually uses the linear methods based on techniques in the time domain, frequency domain, or time-frequency domain. Electroencephalograms (EEGs) contain a wealth of physiological information, have been widely used for the classiﬁcation of sleep stage. Due to the nonlinear, non-stationary, and multifractal characteristics of EEGs, some nonlinear methods have been used to extract features of sleep stages in recent years, such as complexity, multifractal theory, and chaos theory. The Wavelet Leader Multifractal Formalism (WLMF) of the multifractal theory is widely applied to diﬀerent physiological signals. The current researches focus on discussing the mean H¨older exponent (h0) and the width of the multifractal singularity spectrum (WD(h)) estimated by the WLMF method. However, in the ﬁeld of sleep staging, a number of researches focused on h0, but few studies on WD(h). Results: This paper aims to assess the multifractal characteristic for sleep EEG time series from the Sleep-EDF Expanded Database by the WLMF method. In the young group, the mean h0 increased from the Wake stage to the S3 stage (p<0.01). So did the elderly group (p<0.001). WD(h) of the Wake stage was less than that of the S3 stage for the young group, and this relationship was reversed for the elderly group(χ2=13.769, df=1, p<0.001). Gender did not aﬀect, with statistical signiﬁcance, WD(h) of the Wake stage and the S3 stage (χ2=0.085, df=1, p=0.608), nor did the brain region (χ2=3.137, df=1, p=0.078). Conclusions: The result shows that WD(h) was inﬂuenced by aging. The gender and location of brain regions did not show signiﬁcant inﬂuence on the multifractal characteristics of wakefulness and sleeping. This ﬁnding extends the application of the multifractal singularity spectrum on sleep staging, and raises a fundamental question on what might be the underlying mechanisms of the WD(h) reversion.

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EOG and EMG: Two Important Switches in Automatic Sleep Stage Classification

Cited by 31 publications

References 3 publications

Classification of awake, REM, and NREM from EEG via singular spectrum analysis

Classification of awake, REM, and NREM from EEG via singular spectrum analysis

A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages

Wavelet Leader Based Multifractal Analysis of Sleep Electroencephalogram

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