• Sleep Medicine and Psychophysiology
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• v.9 no.1
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• pp.9-13
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• 2002

Alzheimer's disease (AD) is one of the most common and devastating dementing disorders of old age. Most AD patients showed significant alternation of sleep structure as well as cognitive deficit. Typical findings of sleep architecture in AD patients include lower sleep efficiency, higher stage 1 percentage, and greater frequency of arousals. The slowing of EEG activity is also noted. Abnormalities in REM sleep are of particular interest in AD because the cholinergic system is related to both REM sleep and AD. Several parameters representing REM sleep structure such as REM latency, the amount of REM sleep, and REM density are change in patients with AD. Especially, measurements of EEG slowing during tonic REM sleep can be used as an EEG marker for early detection of possible AD. In addition, a structural defect in the suprachiasmatic nucleus is suggested to cause various chronobiological alternations in AD. Most of alternations related to sleep make sleep disturbances common and disruptive symptoms of AD. In this article, the author reviewed the alternation of sleep structure and circadian rhythm in AD patients.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.27-32
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• 2011

This paper presents a methodology for classifying sleep disturbance using electroencephalogram (EEG) signal at sleep stage 2 and principal component analysis. For extracting initial features, fast Fourier transforms(FFT) were carried out to remove some noise from EEG signal at sleep stage 2. In the second phase, we used principal component analysis to reduction from EEG signal that was removed some noise by FFT to 5 features. In the final phase, 5 features were used as inputs of NEWFM to get performance results. The proposed methodology shows that accuracy rate, specificity rate, and sensitivity were all 100%.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.140-147
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• 2021

EEG(electroencephalogram) are measured to accurately determine the level of sleep in various sleep examinations. In general, measurements are more accurate as the number of sensor channels increases. EEG can interfere with sleep by attaching electrodes to the skin when measuring. It is necessary for self sleep care to select the minimum number of EEG channels that take into account both the user's discomfort and the accuracy of the measurement data. In this paper, we proposed a sleep stage analysis model based on machine learning and conducted experiments for using from one channel to four channels. We obtained estimation accuracy for sleep stage as following 82.28% for one channel, 85.77% for two channels, 80.33% for three channels and 68.87% for four channels. Although the measurement location is limited, the results of this study compare the accuracy according to the number of channels and provide information on the selection of channel numbers in the EEG sleep analysis.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.69-74
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• 1997

Sleep deprivation may affect the brain functions such as cognition and, consequentoy, dynamics of the EEG. we examiced the effects of sleep deprivation on chaoticity of EEG. Five volunteers were sleep-deprived over a period of 24 hours, They were checked by EEG during two days, the first day of baseline period, EEGs were reorded form 16 channels for nonlinear analysis. We dmployed a method of minimum cmbedding dimension to calculate the first positive Lyapunov exponent. For limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Our results show that the sleep deprived volunteers had lower values of the first positive Lyapunov exponent at ten channels (Fp$\_$1/, F$\_$4/, F$\_$8/, T$\_$4/, T$\_$5/, C$\_$3/, C$\_$4/, P$\_$3/, p$\_$4, O$\_$1/) compared with the values of baseline periods. These results suggested that sleep deprivation leads to decreawe of chaotic activity in brain and impairment of the information processing in the brain. We suggested that nonlinear analysis of the EEG before and after sleep deprivation may offer fruitful perspectives for understanding the role o f sleep deprivation on the brain function.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1491-1499
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• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• Journal of Ginseng Research
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• v.34 no.1
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• pp.30-40
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• 2010

The present investigation was conducted to evaluate the regulation of sleep architecture by the red ginseng water extract (RGE) in acutely and chronically restraint stressed rats. Adult rats were fitted with sleep.wake recording electrodes. Following post-surgical recovery, rats were extensively habituated for freely moving polygraphic recording conditions. Polygraphic signs of sleep-wake activities were recorded for 24 h after RGE administration and induction of stress and were analyzed to understand the regulation of sleep architecture. Acute stress decreased wakefulness and increased total sleep, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep in both the daytime and nighttime recording. RGE shortened the daytime NREM and REM sleep, without changing the wakefulness and total sleep. RGE increased nighttime wakefulness, and decreased total, NREM and REM sleep. Chronic stress increased wakefulness and decreased total sleep in the daytime recording, and increased REM and decreased NREM sleep in both the day and night time recording. RGE ameliorated chronic stress and induced alterations of REM and NREM sleep in the day and night time sleep architecture. Acute and chronic stress could also induce alternations in cortex electroencephalogram (EEG) recording during NREM, REM sleep and wakefulness. These findings suggest that RGE may modulate the sleep behavior in acutely and chronically stressed rats and the ameliorating effect of RGE on the sleep architecture may involve in modulation of $\alpha$-, $\theta$- and $\delta$- wave activities of the cortical EEG.

• Clinical and Experimental Pediatrics
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• v.51 no.8
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• pp.861-867
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• 2008

Purpose : We carried out this study to determine if there is any difference in the occurrence rate of the epileptiform discharge between awake EEG and sleep EEG and if there are any factors influencing on the occurrence rate of EEG. Methods : This study included 178 epileptic children who had visited neurology clinic of the department of pediatrics, Pusan National University Hospital from July 2005 to July 2006. The medical and EEG records of these children who had had both awake EEG and sleep EEG were reviewed. We analysed the occurrence rate of the epileptiform discharge between awake EEG and sleep EEG. We investigated the related clinical factors which included sex, seizure types, underlying causes, age at first seizure, antiepileptic drug (AED) medication, age at recording, and background activity. Results : Among 178 epileptic children, 91 patients (51.1%) showed epileptiform discharge in awake or sleep states, 10 patients (11.0%) abnormal only in awake, 40 patients (44.0%) abnormal only in sleep, 41 patients (45.0%) abnormal in both awake EEG and sleep EEG. The occurrence rate of sleep EEG was 81 of 178 patients (45.5%) which was more than that of the awake EEG (28.7%) (P<0.001). The occurrence rate of sleep EEG is more than that of the awake EEG regardless of sex and underlying causes. But there is no significant difference from awake EEG and sleep EEG in finding the epileptiform discharge in the patient with generalized seizure, younger than 5 years old at first seizure, younger than 10 years old at recording, no antiepileptic medication, and abnormal background activity. Conclusion : The sleep EEG is thought to be more helpful in the diagnosis of childhood epilepsy.

• Science of Emotion and Sensibility
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• v.1 no.1
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• pp.69-78
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• 1998

Sleep deprivation may affect the brain functions such as cognition and consequently, dynamics of the BEG. We examined the effects of sleep deprivation on chaoticity of the EEG. Five volunteers were sleep-deprived over a period of 24 hours They were checked by EEG during two days. thc first day of baseline period and the second day of total sleep deprivation period. EEGs were recorded from 16 channels for nonlinear analysis. We employed a method of minimum embedding dimension to calculate the first positive Lyapunov exponent. Fer limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Our results show that the sleep deprived volunteers had lower values of the first positive Lyapunov exponent at ten channels (Fp1, F4. F8. T4, T5. C3, C4. P3. P4. O1) compared with the values of baseline periods. These results suggested that sleep deprivation leads to decrease of chaotic activity in brain and impairment of the information processing in the brain. We suggested that nonlinear analysis of the EEG before and after sleep deprivation may offer fruitful perspectives for understanding the role if sleep and the effects of sleep deprivation on the brain function.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• Sleep Medicine and Psychophysiology
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• v.15 no.2
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• pp.71-76
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• 2008

Introduction: In this study, we compared sleep structure, EEG characteristic of pediatric obstructive sleep apnea (OSA) and normal controls which were matched in sex and age. Methods: Fifteen children (male：female=4：11) who complained snoring and were suspected to have sleep apnea and their age and sex matched normal controls (male：female=5：10) have been done nocturnal polysomnography (NPSG). Sleep parameters, sleep apnea variables and relative spectral components of EEG from NPSG have been compared between both groups. Results: Pediatric OSA group were distinguished from normal controls in terms of apnea index, respiratory disturbance index and nadir of oxyhemoglobulin desaturation. Pediatric OSA group showed increased percent of sleep stage 1, decreased rapid eye movement sleep percent and increased delta power in O1 EEG channel. However other sleep parameters and spectral powers were not different between two groups. Conclusion: In pediatric OSA group, sleep structure parameter disruption may be not prominent as the previous studies for adult OSA group because of including mild OSA data in diagnostic criteria. In addition, EEG changes might not be distinct due to low arousal index compared to adult OSA patients. We can observe general characteristics and particularity of pediatric OSA through this study.