Automatic recognition of alertness and drowsiness from EEG by an artificial neural network

Vučković, Aleksandra; Radivojević, Vlada; Chen, Andrew C. N.; Popović, Dejan B.

doi:10.1016/s1350-4533(02)00030-9

Cited by 178 publications

(80 citation statements)

References 23 publications

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“…This task is difficult, subjective and is often an exasperating time consuming process, thus leading to a low reliability and differences among scorers [10,11]. In order to effectively address these issues, automatic classification schemes have been progressively introduced as an objective way to assist the experts during this process [12,13].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…However, this study employed sleep-deprived subjects, performing the experiment on the morning following the subjects lack of sleep. A neural network has also been used as a classification method to detect drowsiness [19]. Regarding the previous studies results, we should consider using a gazing time parameter to test the drowsiness detection performance using a classification method or machine learning in the next step of our investigation.…”

Section: Discussionmentioning

confidence: 99%

“…In a similar way, we also realized that only a limited number of drowsiness investigations have utilized head-mounted measurement techniques. Driving in a drowsy condition was examined by certain researchers [18,19] using different measurements. Even though the accuracy reached almost 99%, these results were obtained in sleep-deprived subjects rather than under a condition where natural drowsiness occurred.…”

mentioning

confidence: 99%

Gazing Time Analysis for Drowsiness Assessment Using Eye Gaze Tracker

Rumagit¹,

Akbar²,

Igasaki³

2017

TELKOMNIKA

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“…Lately, ANN has been shown to provide a superior alternative mechanism for modeling non-linear systems [10][11][12][13][14] . Application of ANNs is similar to conventional non-linear modeling techniques that require a model-structure design and parameter-estimating cycle to calibrate the model but ANNs use more general modeling approach, i.e.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Network to Optimize The pH Response Range of Chlorophenol Red

Wong

Ahmad

Heng

2017

AJSTD

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Artificial Neural Network (ANN) had been used in this study to extend the response range of the pH indicator. The input from absorbance values of the absorbance spectra of chlorophenol red at different pH was used to train the ANN. During the training process, the coefficient values of the ANN will be adjusted to obtain the desire output. In this research, back propagation algorithm had been used for optimizing the response range of the pH indicator chlorophenol red in solution. The result indicates that the use of ANN enable the pH response range to be extended from 4.8-6.8 to 1.0-10.0.Keywords : Artificial neural network, back-propagation algorithm, pH indicator,chlorophenol red. INTRODUCTIONChlorophenol red is a pH indicator, which gradually changes its colour over a range called "visual transition interval" 1 . Most of the pH indicators determine the pH changes indirectly in limited linear dynamic range, often 2-4 pH units only. Chlorophenol red indicator changes its colour from yellow to purple in a narrow interval of 4.0-7.0 pH units. To solve this problem especially in optical fiber optic pH sensor research, a lot of approach has been used such as using multiple pH indicators 2,3 , the use of indicators with multiple steps of acid dissociation and ANN technique 4-6 .The original works on ANN were published more than 50 years ago by McCulloch and Pitts 7,8 and Webb 9 . Lately, ANN has been shown to provide a superior alternative mechanism for modeling non-linear systems [10][11][12][13][14] . Application of ANNs is similar to conventional non-linear modeling techniques that require a model-structure design and parameter-estimating cycle to calibrate the model but ANNs use more general modeling approach, i.e. model structure need not to be defined explicitly 12 .

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Automatic recognition of alertness and drowsiness from EEG by an artificial neural network

Cited by 178 publications

References 23 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

Gazing Time Analysis for Drowsiness Assessment Using Eye Gaze Tracker

The Use of Artificial Neural Network to Optimize The pH Response Range of Chlorophenol Red

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