Detecting rapid eye movement sleep using a single EEG signal channel

Lin, Nan-Hung; Hsu, Chung‐Yao; Luo, Yuxi; Nagurka, Mark L.; Sung, Jia-Li; Hong, Chih-Yuan; Yen, Chen‐Wen

doi:10.1016/j.eswa.2017.06.017

Cited by 6 publications

(3 citation statements)

References 72 publications

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“…This work uses multiclass SVM with a one versus one mechanism, separating each pair of classes against each other and using a majority voting scheme to determine the output. The SVM classifier can be written as in (11):…”

Section: Support Vector Machinementioning

confidence: 99%

“…Previous studies investigating differences in EEG signals involved in determining intelligent quotient (IQ) [10] as well as EEG related problems such as sleep studies [11], epileptic, mental task [12], mental imaginary [13], motor imaginary [14], brain-computer interface [15], epilepsy [16], autism spectrum disorder [17] and learning disabilities [18]. This is done by applying several machine learning such as multilayer perceptron (MLP), linear discrimination analysis (LDA) and artificial neural network (ANN) to identify this symptom by examining the EEG signals.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Zainuddin

Mansor

Lee

et al. 2022

IJECE

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<span lang="EN-US">Dyslexia is a form of learning disability that causes a child to have difficulties in writing alphabets, reading words, and doing mathematics. Early identification of dyslexia is important to provide early intervention to improve learning disabilities. This study was carried out to differentiate EEG signals of poor dyslexic, capable dyslexic, and normal children during writing using machine learning and deep learning. three machine learning algorithms were studied: k-nearest neighbors (KNN), support vector machine (SVM), and extreme learning machine (ELM) with input features from coefficients of beta and theta band power extracted using discrete wavelet transform (DWT). As for the deep learning (DL) algorithm, long short-term memory (LSTM) architecture was employed. The kernel parameters of the classifiers were optimized to achieve high classification accuracy. Results showed that db8 achieved the greatest classification accuracy for all classifiers. Support vector machine with radial basis function kernel yields the highest accuracy which is 88% than other classifiers. The support vector machine with radial basis function kernel with db8 could be employed in determining the dyslexic children’s levels objectively during writing.</span>

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Section: Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Zainuddin

Mansor

Lee

et al. 2022

IJECE

View full text Add to dashboard Cite

show abstract

“…Mousavi et al proposed an approach for classification of two to six sleep stages by constructing a convolutional neural network (CNN) with single-channel EEG signals [14]. Furthermore, we used a single EEG channel to detect REM sleep and slow-wave sleep and achieved accuracies of 93% and 96%, respectively [15,16]. Because the autonomic nervous system state is associated with sleep state, which can be obtained from ECG signals, ECG is also a popular signal for sleep scoring [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep-stage scoring based on photoplethysmographic signals

Wu¹,

Yang²,

Pan³

et al. 2020

Physiol. Meas.

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Objective: Sleep-stage scoring is important for sleep-quality evaluation and the diagnosis of related diseases. In this study, an automatic sleep-stage scoring method using photoplethysmographic (PPG) signals was proposed. Approach: To construct the classification model, we extracted 14 time-domain features, 17 frequency-domain features, and 20 pulse rate variability (PRV) features along with four SpO2 features from PPG signals. An artificial neural network classifier was used to integrate the results of ten binary support vector machine classifiers and realise sleep-stage classification. Leave-one-subject-out validation was applied to evaluate our proposed model. Main results: Thirty-one subjects were enrolled in the study, in which 21 subjects were with high sleep quality (sleep efficiencies ⩾85%). Our model achieved accuracies of 57% (κ = 0.39), 62% (κ = 0.41), and 78% (κ = 0.54) for the classification of five sleep stages (wake, N1, N2, N3, and rapid eye movement (REM) sleeps), four sleep stages (wake, light, deep, and REM sleeps) and three sleep stages (wake, non-rapid eye movement (NREM), and REM sleeps), respectively. For the remaining ten subjects with poor sleep quality, the results came to 55% (κ = 0.39), 62% (κ = 0.43), and 75% (κ = 0.52). Significance: The satisfactory performance of our proposed model reveals the potential of PPG signals for sleep-stage scoring, which may contribute to automatic sleep monitoring in the home environment.

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An expert system gap analysis and empirical triangulation of individual differences, interventions, and information technology applications in alertness of railroad workers

Rodger

2020

Expert Systems with Applications

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Detecting rapid eye movement sleep using a single EEG signal channel

Cited by 6 publications

References 72 publications

Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Machine learning and deep learning performance in classifying dyslexic children’s electroencephalogram during writing

Automatic sleep-stage scoring based on photoplethysmographic signals

An expert system gap analysis and empirical triangulation of individual differences, interventions, and information technology applications in alertness of railroad workers

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