Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG

Tağluk, M. Emin; Sezgin, Necmettin; Akın, Mehmet

doi:10.1007/s10916-009-9286-5

Cited by 104 publications

(53 citation statements)

References 20 publications

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“…They range from simple linear discriminant analysis (LDA) to non-linear and highly complex Gaussian mixture model-based classifiers [20,21]. For classification of different sleep states, several traditional methods, namely LDA [18,22], neural networks [23,24], support vector machines (SVM) [25,9,26,27,6], knearest neighbor (k-NN) [16], hidden Markov model [28], fuzzy systems [29,30], etc., are proposed for distinguishing between different sleep stages. The common to all traditional classifiers is that they have only one classifier.…”

Section: Introductionmentioning

confidence: 99%

Ensemble SVM Method for Automatic Sleep Stage Classification

Aličković

Subaşı

2018

IEEE Trans. Instrum. Meas.

228

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Abstract-Sleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep disorders. Automated sleep scoring is crucial, since the large volume of data should be analyzed visually by the sleep specialists which is burdensome, time-consuming tedious, subjective, and error-prone. Therefore, automated sleep stage classification is crucial step in sleep research and sleep disorder diagnosis. In the present article, a robust system, consisting of three modules, is proposed for automated classification of sleep stages from single channel EEG. In the first module, signals taken from Pz-Oz electrode were denoised using multiscale principal component analysis. In the second module, the most informative features are extracted using discrete wavelet transform (DWT) and then, statistical values of DWT sub-bands are calculated. In the third module, extracted features were fed into an ensemble classifier, which can be called as rotational support vector machine (RotSVM). The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM. The sensitivity and accuracy values across all subjects were 84.46% and 91.1% respectively for five stage sleep classification with Cohen's kappa coefficient of 0.88. Obtained classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single channel EEG, and can be used effectively in medical and home-care application.

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Section: Introductionmentioning

confidence: 99%

Ensemble SVM Method for Automatic Sleep Stage Classification

Aličković

Subaşı

2018

IEEE Trans. Instrum. Meas.

228

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“…Usually in the neural networks designed for such computations are much simpler structures and possess the abilities of learning and reacting [12]. The primary features of neural networks which are adopted for computational purpose are its ability to adapt as well as its characteristic of non-algorithm and parallel-distributed memory [13].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review

Farooq¹

2017

IJSSN

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Abstract:Sleep is an essential element for an individual's well-being and is considered vital for the overall mental and physical heath of a person. Sleep can be considered as a virtual detachment of an individual from his environment. In normal humans, about 30% of their life-time is spent for sleep. Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains. Sleep scoring is under taken by the examination and visual inspection of polysomnograms (PSG) done by sleep specialist. PSG is specialty test, the conduction of which includes the recording of various physiological signals. The signals obtained are processed using digital processing tools so as to extract information. Soft computing techniques are used to analyze the signals. ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparations of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present. The high performances observed with systems based onneural networks highlight that these tools may be act new tools in the field of sleep research. In this scenario we are surmised the review regarding the computer assisted automatic scoring of sleep and soft computing technique Artificial Neural Network.

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“…В развитых странах BIS-мониторинг стал золотым стан-дартом контроля состояния сознания пациента при об-щей анестезии и интенсивной терапии. На лоб пациента устанавливается сенсор, с помощью которого регистри-руется электроэнцефалограмма [20]. BIS-система обраба-тывает поступающий сигнал и вычисляет BIS-индекс, число от 0 до 100, которое позволяет судить о степени со-знания.…”

Section: стоматология 6 2014unclassified

Diagnostics and treatment of patients with II class malocclusion associated with obstructive sleep apnea syndrome

Nabiev¹,

Dobrodeev²,

Peng³

et al. 2014

Stomat.

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ОБЗОРЫХирургическое лечение зубочелюстных аномалий имеет более чем столетнюю историю. Однако, несмотря на значительный прогресс в развитии челюстно-лицевой хирургии, в последние десятилетия остаются актуальны-ми вопросы диагностики и лечения больных с сочетанны-ми деформациями челюстей, сопровождающимися нару-шениями анатомических структур не только зубочелюст-ной системы, но и близлежащих областей [13,28,29,33].Одними из самых сложных в структуре аномалий зу-бочелюстной системы признаны пациенты с дистальным прикусом, так как до сих пор подробно не изучены клини-ческие проявления аномалий, связанных со II классом окклюзии по Энглю, состояние близлежащих анатомиче-ских образований, анатомическое положение языка, же-вательных мышц, рото-и носоглотки. В научных трудах не затронуты особенности проявления синдрома обструк-тивного апноэ во сне, размеры и положение дыхательных путей при данном виде патологии [28,29,32].Ярким клиническим симптомом этой группы зубоче-люстных аномалий является нарушение функции дыха-ния и сна, которые в литературе определены термином «синдром обструктивного апноэ сна» (СОАС).СОАС -это заболевание, характеризующееся нали-чием храпа, периодическим спадением дыхательных пу-тей на уровне глотки и прекращением легочной вентиля-ции, снижением уровня кислорода в крови, грубой фраг-ментацией сна и избыточной дневной сонливостью [8][9][10]12].СОАС обнаруживают у 3-7% населения развитых стран мира старше 30 лет. В США, по данным Американ-ской Академии Медицины Сна, от проблем, связанных с СОАС, ежегодно погибает более 38 000 человек.

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Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG

Cited by 104 publications

References 20 publications

Ensemble SVM Method for Automatic Sleep Stage Classification

Ensemble SVM Method for Automatic Sleep Stage Classification

A Novel Computer Assisted Automatic Sleep Scoring System by Employing Artificial Neural Network–A review

Diagnostics and treatment of patients with II class malocclusion associated with obstructive sleep apnea syndrome

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