RETRACTED ARTICLE: A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

Dursun, Mehmet; Özşen, Seral; Yücelbaş, Cüneyt; Yücelbaş, Şule; Tezel, Gülay; Küççüktürk, Serkan; Yosunkaya, Şebnem

doi:10.1007/s00521-016-2578-z

Cited by 20 publications

(8 citation statements)

References 30 publications

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“…Sleep stage detection and recognition is an important basis for measuring and evaluating sleep quality. Sleep stage detection and recognition is usually conducted with polysomnography (PSG), including electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), electrocardiogram (ECG), chest and abdomen movements, pulse oxygenation, and snoring [ 13 , 14 ]. These technologies involve the use of a considerable number electrodes, which is very inconvenient for the measurement and treatment of sleep disorders and may bring physical and psychological interference to patients, and even affect the quality of sleep [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Invasive EEG causes irreversible damage to the brain, whereas non-invasive EEG (scalp EEG) has very low spatial resolution [ 5 ]. The use of EMG and EOG in detecting the sleep has undergone considerable progress but these methods rely on too many physiological traits and physiological response in the time dimension tends to be delayed by 5–6 s, which limits the time dynamics of the brain’s stimulus response; the low temporal and spatial resolution may also ignore some important information in the signal [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Geng

Yang

Cai

et al. 2020

Entropy

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The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Geng

Yang

Cai

et al. 2020

Entropy

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“…This method estimates alertness level based on eye movement [78]. Specifically, it measures the electrical signal representing the potential difference between the cornea and retina of a subject's eye [79], [80]. In this method, an electrode is placed near the end of each eye and another is placed at top of the eye.…”

Section: F Electro-oculogram (Eog)mentioning

confidence: 99%

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

2019

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Drowsiness/sleepiness is a serious issue that needs to be addressed for improvement in the safety of road driving. Past statistical data on road accidents has shown enormous increases in car crashes due to drowsy/sleepy feelings. This study comprehensively summarizes all aspects of the drowsy state and its effects during car driving: its symptoms, causes, preventive actions, car accident statistics, sleep stages, and the behavioral, physiological and neural activation changes occurring during wakefulness and in the drowsy state. It considers drivers' behavioral data and corresponding methodologies for its analysis, the biomedical signals of the human body (including neuronal signals in the forms of electrical and hemodynamic responses), and their use for drowsiness detection. All of the existing methodologies, their uses and pros and cons, are comprehensively summarized. A detailed survey of the data published by neuro-imaging methodology-, physiological signal-and behavioral methodology-based studies in addition to studies using electro-mechanical installed sensors are statistically and theoretically summarized. Additionally, the neuronal activity occurring during the drowsy and awake states are analyzed, and the important contributions of fNIRS, fMRI and EEG in this context are discussed in detail. Differing existing drowsinessdetection systems installed in popular car brands also are reviewed. Finally, the remaining challenges and future suggestions for drowsiness-detection systems are summarized as well.

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“…Pada pengolahan sinyal EEG, sinyal EOG menjadi noise yang cukup signifikan dimana amplitude sinyal EOG yang lebih tinggi dibandingkan dengan sinyal EEG [7]. Untuk itu diperlukan metode khusus untuk menghilangkan sinyal EOG pada rekaman sinyal EEG [8]. Sementara itu pada pengolahan sinyal EMG, pada kasus tertentu tidak semua pasien bisa menggunakan sinyal EMG utamanya pada pasien kelumpuhan total [9].…”

Section: B Pengolahan Sinyal Eogunclassified

Pengembangan Sistem Pengendali Kursor Menggunakan Sinyal Elektrookulogram (EOG)

Perdana

Suratman

Rizal

2019

jtim

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Amyotrophic Lateral Sclerosis (ALS) is an illness due to lack of nourishment in human motoric nerves. This illness causes the sufferer a loss in motoric movement. Thankfully there is still an organ that move well regardless the illness, and that is eye movement. Eye tracking method have been applied to controlling computer. Majority of eye tracking methods are divided into two methods, video-oculography (VOG), and electrooculography (EOG). VOG used camera as video recorder and processed using image processing to track eye movement. EOG used skin electrode that were usually used in electrocardiography, which could detect electrical activity on the back of eye. The principle in this research is to design a cursor controlling system using EOG sensor and classified the signal using simple thresholding method. The result would be cursor movement based on eye movement. Purpose of this final assignment is to design a cursor controlling system based EOG sensor. The experimental results yield an accuracy of 98% for the cursor movement controlling. The system can control the direction of the cursor's movement but cannot control other activities of the cursor.

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RETRACTED ARTICLE: A new approach to eliminating EOG artifacts from the sleep EEG signals for the automatic sleep stage classification

Cited by 20 publications

References 30 publications

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

Pengembangan Sistem Pengendali Kursor Menggunakan Sinyal Elektrookulogram (EOG)

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