• Sleep Medicine and Psychophysiology
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• v.29 no.1
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• pp.1-3
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• 2022

Sleep is associated with Alzheimer's dementia. Many previous researches have shown that inadequate sleep is one of the risk factors that predict Alzheimer's dementia. The causal mechanism of this association is not clear. Slow wave sleep and REM sleep are critical stages in memory consolidation, and by sequential hypothesis both stages are important. Deposition of amyloid beta and tau, the main pathology of Alzheimer's dementia, are also associated with sleep. This review provides the association of sleep and Alzheimer's dementia, and future research is necessary to examine the specific mechanism of this association between sleep and Alzheimer's dementia, which may lead to an early intervention in sleep.

• Clinical and Experimental Pediatrics
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• v.50 no.8
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• pp.711-717
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• 2007

Sleep is a vital, highly organized process regulated by complex systems of neuronal networks and neurotransmitters. Normal sleep comprises non-rapid eye movement (NREM) and REM periods that alternate through the night. Sleep usually begins in NREM and progresses through deeper NREM stages (2, 3, and 4 stages), but newborns enter REM sleep (active sleep) first before NREM (quiet sleep). A period of NREM and REM sleep cycle is approximately 90 minutes, but newborn have a shorter sleep cycle (50 minutes). As children mature, sleep changes as an adult pattern: shorter sleep duration, longer sleep cycles and less daytime sleep. REM sleep is approximately 50% of total sleep in newborn and dramatically decreases over the first 2 years into adulthood (20% to 25%). An initial predominant of slow wave sleep (stage 3 and 4) that peaks in early childhood, drops off abruptly after adolescence by 40% from preteen years, and then declines over the life span. The hypothalamus is recognized as a key area of brain involved in regulation of sleep and wakefulness. The basic function of sleep largely remains elusive, but it is clear that sleep plays an important role in the regulation of CNS and body physiologic processes. Understanding of the architecture of sleep and basic mechanisms that regulate sleep and wake cycle are essential to evaluate normal or abnormal development of sleep pattern changes with age. Reduction or disruption of sleep can have a significant impact on daytime functioning and development, including learning, growth, behavior, and emotional regulation.

• Women's Health Nursing
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• v.20 no.3
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• pp.195-203
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• 2014

Purpose: The objective of this study was to investigate the relationship between fatigue, sleep disturbance, and gestational stress in women during late stage of pregnancy. Methods: This study was conducted with 113 healthy pregnant women at gestational age greater than 27 weeks who were registered at community health centers and received prenatal care. A structured questionnaire regarding fatigue, sleep disturbance, and gestational stress was used. The data was analyzed using a t-test, an ANOVA, and Pearson correlation coefficients. Results: The subjects with unplanned pregnancies and irregular exercise patterns showed a higher level of fatigue than those with planned pregnancies and regular exercise patterns. Pregnant women with caffeine intake manifested higher levels of gestational stress and sleep disturbance than those without. The levels of sleep disturbance and gestational stress increased as the fatigue levels increased. The fatigue levels increased with increased levels of sleep disturbance. Conclusion: Planned pregnancy, regular exercise patterns, and caffeine intake were related with fatigue, sleep disturbance, and gestational stress in women during late stages of pregnancy. Fatigue, sleep disturbance, and gestational stress had close associations to each other. In the future, such results should guide development of nursing intervention programs for women in late stages of pregnancy.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.21-26
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• 2017

In this paper, we suggest an automated sleep scoring method using machine learning algorithms on accelerometer data from a wristband device. For an experiment, 36 subjects slept for about eight hours while polysomnography (PSG) data and accelerometer data were simultaneously recorded. After the experiments, the recorded signals from the subjects were preprocessed, and significant features for sleep stages were extracted. The extracted features were classified into each sleep stage using five machine learning algorithms. For validation of our approach, the obtained results were compared with PSG scoring results evaluated by sleep clinicians. Both accuracy and specificity yielded over 90 percent, and sensitivity was between 50 and 80 percent. In order to investigate the relevance between features and PSG scoring results, information gains were calculated. As a result, the features that had the lowest and highest information gain were skewness and band energy, respectively. In conclusion, the sleep stages were classified using the top 10 significant features with high information gain.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.9-14
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• 2003

Sleep stages have been useful indicator to check a person's comfortableness in a sleep, But the traditional method of scoring sleep stages with polysomnography based on the integrated analysis of the electroencephalogram(EEG), electrooculogram(EOG), electrocardiogram(ECG), and electromyogram(EMG) is too restrictive to take a comfortable sleep for the participants, While the sympathetic nervous system is predominant during a wakefulness, the parasympathetic nervous system is more active during a sleep, Cardiovascular function is controlled by this autonomic nervous system, So, we have interpreted the heart rate variability(HRV) among sleep stages to find a simple method of classifying sleep stages, Six healthy male college students participated, and 12 night sleeps were recorded in this research, Sleep stages based on the "Standard scoring system for sleep stage" were automatically classified with polysomnograph by measuring EEG, EOG, ECG, and EMG(chin and leg) for the six participants during sleeping, To extract only the ECG signals from the polysomnograph and to interpret the HRV, a Sleep Data Acquisition/Analysis System was devised in this research, The power spectrum of HRV was divided into three ranges; low frequency(LF), medium frequency(MF), and high frequency(HF), It showed that, the LF/HF ratio of the Stage W(Wakefulness) was 325% higher than that of the Stage 2(p＜.05), 628% higher than that of the Stage 3(p＜.001), and 800% higher than that of the Stage 4(p＜.001), Moreover, this ratio of the Stage 4 was 427% lower than that of the Stage REM (rapid eye movement) (p＜.05) and 418% lower than that of the Stage l(p＜.05), respectively, It was observed that the LF/HF ratio decreased monotonously as the sleep stage changes from the Stage W, Stage REM, Stage 1, Stage 2, Stage 3, to Stage 4, While the difference of the MF/(LF+HF) ratio among sleep Stages was not significant, it was higher in the Stage REM and Stage 3 than that of in the other sleep stages in view of descriptive statistic analysis for the sample group.

• Sleep Medicine and Psychophysiology
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• v.14 no.1
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• pp.42-48
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• 2007

Introduction: Detrended fluctuation analysis (DFA) is used as a way of studying nonlinearity of EEG. In this study, DFA is applied on sleep EEG of normal subjects to look into its nonlinearity in terms of EEG channels and sleep stages. Method: Twelve healthy young subjects (age：$23.8{\pm}2.5$ years old, male：female=7：5) have undergone nocturnal polysomnography (nPSG). EEG from nPSG was classified in terms of its channels and sleep stages and was analyzed by DFA. Scaling exponents (SEs) yielded by DFA were compared using linear mixed model analysis. Results: Scaling exponents (SEs) of sleep EEG were distributed around 1 showing long term temporal correlation and self-similarity. SE of C3 channel was bigger than that of O1 channel. As sleep stage progressed from stage 1 to slow wave sleep, SE increased accordingly. SE of stage REM sleep did not show significant difference when compared with that of stage 1 sleep. Conclusion: SEs of Normal sleep EEG showed nonlinear characteristic with scale-free fluctuation, long-range temporal correlation, self-similarity and self-organized criticality. SE from DFA differentiated sleep stages and EEG channels. It can be a useful tool in the research with sleep EEG.

• Korean Journal of Clinical Laboratory Science
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• v.43 no.4
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• pp.188-193
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• 2011

Obstructive sleep apnea (OSA) is the most common type of sleep apnea and is caused by obstruction of the upper airway. Since it is closely related to sleep parameter and body indices, the study was focused on the relationship with them. The results of polysomnography (PSG) in obstructive sleep apnea was done at ENT department of Ewha women university Mokdong hospital from March to September 2010 with 52 subjects (male 35, female 17). The leads were placed to measure electroencephalogram (EEG), electrooculogram (EOG), mandibular and anterior tibialis electromyogram (EMG), airflow in nasal and oral cavity, chest and abdominal breathing pattern, snoring sound and arterial oxygen saturation ($SpO_2$) level. From sleep parameter and body indices of adult obstructive sleep apnea compared to normal adult revealed that age (p<0.01) and snoring sound (p<0.05) were increased, stage 1 sleep (p<0.01) was increased, the deeper stages (3&4) of sleep (p<0.05) were reduced. Respiratory disturbance index (RDI) (p<0.01), mean $SpO_2$ (p<0.05) and lowest $SpO_2$ (p<0.01) were also decreased. The correlation analysis from sleep parameter and body indices of OSA showed the positive correlation with age (r=0.463, p<0.001), snoring sound (r=0.278, p<0.05), stage 1 sleep (r=0.391, p<0.01) and RDI (r=0.409, p<0.01), but showed the negative correlation with the deeper stages (3&4) of sleep (r=-0.307, p<0.05), mean $SpO_2$=(r=-0.274, p<0.05) and lowest $SpO_2$ (r=-0.392, p<0.01). This study proves that obstructive sleep apnea and indices have closed related.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.3
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• pp.239-243
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• 2002

Multi-valued decision making for transitional stochastic events was newly derived based on conditional probability of knowledge database which included experts'knowledge and experience. The proposed multi-valued decision making was successfully adopted to the determination of the five levels of the vigilance of a subject during the EEG (electroencephalogram) recording; awake stage (stage W), and sleep stages (stage REM (rapid eye movement), stage 1, stage 2, stage $\sfrac{3}{4}$). Innovative feature of the proposed method is that the algorithm of decision making can be constructed only by use of the knowledge database, inspected by experts. The proposed multi-valued decision making with a mathematical background of the probability can also be applicable widely, in industries and in other medical fields for purposes of the multi-valued decision making.

• Journal of IKEEE
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• v.23 no.4
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• pp.1273-1279
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• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

• Sleep Medicine and Psychophysiology
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• v.5 no.1
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• pp.18-33
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• 1998

Sleep and Epilepsy either represent the opposite and independent spectrum of episodic manifestations from brain or closely interact with each other. Sleep or sleep deprivation may provoke epileptic seizures or activate epileptiform discharges in epilepsy patients whereas epilepsy may alter the sleep structure. Sleep stages are also known to influence pathophysiology of seizures in terms of ictogenesis. In this review, the impact of sleep on epilepsy as well as that of epilepsy on sleep are presented. Additionally the interaction between sleep and epilepsy will be discussed. This review will also comment on the differential diagnosis between nocturnal or sleep-related epilepsy and various sleep disorders. Finally, clinical application of the above perspectives of sleep and epilepsy will be suggested for the purpose of a better management of epilepsies.