• 수면정신생리
• /
• 제16권1호
• /
• pp.16-21
• /
• 2009

In accordance with the development of EEG and polysomnography in the field of sleep research, the sleep onset period (SOP) between wakefulness and sleep has been considered an important part for understanding the physiology of sleep. SOP in the transition from wakefulness to sleep is a gradual process integrating various viewpoints such as behavior, EEG, physiology and subjective report. Particularly, based on understanding of EEG changes during sleep, SOP has been regarded as a pattern of topographical change in specific frequency and specific state in EEG. Studies on quantitative EEG (qEEG) and event-related potential (ERP) have suggested that SOP shows the changes of functional coordination at the specific cortical areas in qEEG and the changes of regular patterns in response to environmental stimulation in ERP. The development of sleep EEG and topographic mapping of EEG is expected to integrate various viewpoints of SOP and clarify the neurophysiologic mechanism of SOP further.

• 수면정신생리
• /
• 제8권1호
• /
• pp.67-71
• /
• 2001

Independent Component Analysis (ICA) is a blind source separation method using unsupervised learning and mutual information theory created in the late eighties and developed in the nineties. It has already succeeded in separating eye movement artifacts from human scalp EEG recording. Several characteristic sleep waves such as sleep spindle, K-complex, and positive occipital sharp transient of sleep (POSTS) can be recorded during sleep EEG recording. They are used as stage determining factors of sleep staging and might be reflections of unknown neural sources during sleep. We applied the ICA method to sleep EEG for sleep waves separation. Eighteen channel scalp longitudinal bipolar montage was used for the EEG recording. With the sampling rate of 256Hz, digital EEG data were converted into 18 by n matrix which was used as a original data matrix X. Independent source matrix U (18 by n) was obtained by independent component analysis method ($U=W{\timex}X$, where W is an 18 by 18 matrix obtained by ICA procedures). ICA was applied to the original EEG containing sleep spindle, K-complex, and POSTS. Among the 18 independent components, those containing characteristic shape of sleep waves could be identified. Each independent component was reconstructed into original montage by the product of inverse matrix of W (inv(W)) and U. The reconstructed EEG might be a separation of sleep waves without other components of original EEG matrix X. This result (might) demonstrates that characteristic sleep waves may be separated from original EEG of unknown mixed neural origins by the Independent Component Analysis (ICA) method.

• 한국멀티미디어학회논문지
• /
• 제24권8호
• /
• pp.1149-1160
• /
• 2021

Lack of sleep time increases risks of fatigue, hypomnesis, decreased emotional stability, indigestion, and dementia. The risks can be reduced by providing eyelid-warming, inducing sleep and improving sleep quality. However, effective warming temperature to an person varies depending on physical condition and the individual. The various types of frequencies can be identified in brain wave from a person and amount of frequencies is also changed continuously before and after sleep. Therefore we can identify the user's sleep stage with brain wave, namely EEG. Effective sleep induction is possible if warming temperature to a person is controlled based on EEG. In this paper, we propose customized warming control techniques based on EEG for a efficient and effective sleep induction. As an experiment, sleep induction effects of standard sleep mask and customized temperature control techniques sleep mask are compared. EEG data and warming temperature were measured in 100 experiments. At customized warming control techniques, experiments showed that the ratio of alpha and theta waves increased by 3.21%p and the time to sleep decreased by 85 seconds. It will contribute to effective sleep induction and performance verification methods in customized sleep mask systems.

• 수면정신생리
• /
• 제25권2호
• /
• pp.82-91
• /
• 2018

목 적 : 본 연구는 전 수면 주기 동안 수면단계에 따른 전체 뇌 영역과 수면 관련 뇌 영역들의 뇌기능 연결망의 변화를 살펴보기 위해 동기화된 뇌파(EEG)-자기기능공명영상(fMRI)를 전 수면 주기 동안 측정하고 신호처리 기법을 사용함으로 수면 단계에 따른 뇌 연결망의 탐구가 가능함을 살펴 보기 위해 수행되었다. 방 법 : 정상 성인 피험자 5인을 대상으로 6~7시간의 수면동안 MRI 기계 안에서 안전도, 심전도, 근전도와 EEG-fMRI를 측정하였고 EEG에 발생한 MRI 자장 변화 잡음과 심박관련 잡음을 제거하였다. fMRI에서는 피험자의 움직임에 의해 발생하는 영상 왜곡을 보정하는 부분볼륨활용기법을 제안하여 사용하였다. 잡음이 제거된 수면중 fMRI에 독립성분분석기법을 적용하여 뇌 전체를 68 영역으로 구획하여 수면 연구에 적합한 뇌 구획 지도를 만들고 이를 바탕으로 각 구획들간의 연결성을 계산하였다. 수면관련 뇌심부 영역을 선택하여 연결망 분석을 수행하였다. 결 과 : 뇌파를 비롯한 수면 생리적 신호들은 잡음 제거의 방법을 이용하게 되면 수면단계설정에 문제가 없으며 수면 단계별 뇌 연결망 연구가 가능함을 보여 주었다. 뇌연결망 분석에서 수면 관련 뇌심부 연결망은 렘과 비렘수면에 따라 다른 특성이 나타나는데 비렘수면에서 전반적으로 높은 연결성을 보였다. 대뇌를 포함한 전체 뇌 연결망의 경우 각성에 비해서 수면 중에 뇌 연결성이 떨어지는 양상을 보였다(Kolmogorov-Smirnov 검정 ; p < 0.05, Bonferroni corrected). 결 론 : 본 연구를 통해서 장시간 수면 EEG-fMRI 측정과 수면단계설정이 가능하고 신호처리 기법을 통해서 보정하게 되면 뇌기능 연결망을 이용한 전체 수면 뇌 연구가 가능함을 시사한다.

• 한국산업정보학회논문지
• /
• 제22권1호
• /
• pp.41-51
• /
• 2017

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

• 수면정신생리
• /
• 제17권2호
• /
• pp.75-84
• /
• 2010

The cyclic alternating pattern (CAP) is a periodic EEG activity in NREM sleep, characterized by sequences of transient electrocortical events that are distinct from background EEG activities. A CAP cycle consists of two periodic EEG features, phase A and subsequent phase B whose durations are 2-60 s. At least two consecutive CAP cycles are required to define a CAP sequence. The CAP phase A is a phasic EEG event, such as delta bursts, vertex sharp transients, K-complex sequences, polyphasic bursts, K-alpha, intermittent alpha, and arousals. Phase B is repetitive periods of background EEG activity. The absence of CAP more than 60 seconds or an isolated phase A is classified as non-CAP. Phase A activities can be classified into three subtypes (A1, A2, and A3), based on the amounts of high-voltage slow waves (EEG synchrony) and low-amplitude fast rhythms (EEG desynchrony). CAP rate, the percentage of CAP durations in NREM sleep is considered to be a physiologic marker of the NREM sleep instability. In insomnia, the frequent discrepancy between self-reports and polysomnographic findings could be attributed to subtle abnormalities in the sleep tracing, which are overlooked by the conventional scoring methods. The conventional scoring scheme has superiority in analysis of macrostructure of sleep but shows limited power in finding arousals and transient EEG events that are major component of microstructure of sleep. But, it has recently been found that a significant correlation exists between CAP rate and the subjective estimates of the sleep quality in insomniacs and sleep-improving treatments often reduce the amount of CAP. Thus, the extension of conventional sleep measures with the new CAP variables, which appear to be the more sensitive to sleep disturbance, may improve our knowledge on the diagnosis and management of insomnia.

• 수면정신생리
• /
• 제14권1호
• /
• pp.42-48
• /
• 2007

목 적：뇌파의 비선형적 특성을 연구하는 방법으로 탈경향 변동분석이 사용되고 있다. 본 연구에서는 정상인 수면 뇌파에 탈경향변동분석을 적용하여 수면뇌파의 비선형적 특성, 채널 별 차이, 수면단계별 차이를 규명하고자 하였다. 방 법：정상인 12명($23.8{\pm}2.5$세, 남：여=7：5)를 대상으로 야간수면다원검사를 시행하였다. 수면다원검사를 통해 얻어진 뇌파를 채널 별, 수면단계별로 나누어 탈경향변동분석 시행 후 여기서 얻어진 축척지수(scaling exponent)를 선형혼합모형 분석을 통해 비교하였다. 결 과：정상인 수면다원검사에서 얻어진 뇌파의 축척지수는 1 내외의 값을 보여 장기-시간적연관성, 자기유사성을 보였다. C3 채널의 축적지수가 O1채널의 축적지수보다 높은 값을 보였다. 수면단계가 진행함에 따라 축적지수는 증가하였으며, 1단계 수면과 렘수면은 축적지수는 통계적 차이를 보이지 않았다. 결 론：정상인 수면 뇌파는 탈경향변동분석에서 무축척요동(scale-free fluctuation), 장기-시간적 관련성(long-range temporal correlation), 자기유사성(self-similarity) 및 스스로 짜여진 고비성(self-organized criticality) 등의 비선형적 특성을 보였다. 탈경향변동분석에서 얻어진 축적지수는 뇌파 채널 별, 수면단계별 차이를 보여 수면 뇌파를 연구하는 중요한 도구로 사용될 수 있다.

• 대한의용생체공학회:의공학회지
• /
• 제24권6호
• /
• pp.487-494
• /
• 2003

수면뇌파의 해석에 있어서 수면단계는 뇌파의 특성파 검출에 특히 중요하다. 수면단계는 여러 수면질환의 진단에 가장 기초적일 단서를 제공한다. 본 연구에서 수면뇌파 신호를 이산 웨이브렛 변환 뿐 만 아니라 퓨우리에 변환, 연속 웨이브렛 변환을 이용해서 해석하였다. 제안된 시스템 방범인 퓨우리에와 웨이브렛은 수면뇌파의 중요한 특성파(유파, 수면방추파, K복합, 구파 REM) 검출을 위해서 수면상태를 분석했다. 수면뇌파 분석에는 Daubechies 웨이브렛 변환 방법과 고속 퓨우리에를 이용했다. 모의실험결과 신경망 시스템이 특성 파형의 분류에 높은 성능을 발휘함을 알 수 있었다.

• Biomolecules & Therapeutics
• /
• 제7권3호
• /
• pp.242-248
• /
• 1999

Diazepam, a benzodiazepine (BDZ) agonist, produces sedation and flumazenil, a BDZ antagonist, blocks these actions. The aim of this study was to examine the effects of BDZs on cortical electroencephalogram (EEG) in rats. The recording electrodes were implanted over the frontal and parietal cortices bilaterally, and the reference and ground electrodes over cerebellum under ketamine anesthesia. To assess the effects of diazepam and flumazenil, rats were injected with diazepam (1 mgHg, i.p.) and/or flumazenil ( 1 mg/kg, i.p.), and the EEG was recorded before and after drugs. Normal awake had theta peak in the spectrum and low amplitude waves, while normal sleep showed large amplitude of slow waves. The powers of delta, theta and alpha bands were increased during sleep compared with during awake. Diazepam reduced the mobility of the rat and induced sleep with intermittent fast spindles and large amplitude of slow activity, and it produced broad peak over betaL band and increased the power of gamma band, which were different from EEG patterns in normal sleep. Saline injection awakened rats and abolished fast spindles for a short period about 2-5 min from EEG pattern during diazepam-induced sleep. Flumazenil blocked both diazepam-induced sleep and decreased the slow activities of delta, theta, alpha and betaL, but not of gamma activity for about 10 min or more. This study may indicate that decrease in power of betaL and betaH bands can be used as the measure of central action of benzodiazepines, and that the EEG parameters of benzodiazepines have to be measured without control over the behavioral state by experimenter.

• 수면정신생리
• /
• 제15권1호
• /
• pp.5-11
• /
• 2008

Sleep is an essential process maintaining the life cycle of the human. In parallel with physiological, cognitive, subjective, and behavioral changes that take place during the sleep, there are remarkable changes in the electroencephalogram (EEG) that reflect the underlying electro-physiological activity of the brain. However, analyzing EEG and relating the results to clinical observations is often very hard due to the complexity and a huge data amount. In this article, I introduce several linear and non-linear tools, developed to analyze a huge time series data in many scientific researches, and apply them to EEG to characterize various sleep states. In particular, the spectral analysis, detrended fluctuation analysis (DFA), and synchrony analysis are administered to EEG recorded during nocturnal polysomnography (NPSG) processes and daytime multiple sleep latency tests (MSLT). I report that 1) sleep stages could be differentiated by the spectral analysis and the DFA ; 2) the gradual transition from Wake to Sleep during the sleep onset could be illustrated by the spectral analysis and the DFA ; 3) electrophysiological properties of narcolepsy could be characterized by the DFA ; 4) hypnic jerks (sleep starts) could be quantified by the synchrony analysis.