• 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.

• 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%.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• 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.

• Journal of Biomedical Engineering Research
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• v.24 no.6 s.81
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• pp.487-494
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• 2003

The sleep stages provides the most basic evidence for diagnosing a variety of sleep diseases. for staging sleep by analysis of EEG(electroencephalogram), it is especially important to detect the characteristic waveforms from EEG. In this paper, sleep EEG signals were analyzed using Fourier transform and continuous wavelet transform as well as discrete wavelet transform. Proposeed system methods. Fourier and wavelet for detecting of important characteristic waves(hump, sleep spindles. K-complex, hill wave, ripple wave) in sleep EEG. Sleep EEG data were analysed using Daubechies wavelet transform method and FFT method. As a result of simulation, we suggest that our neural network system attain high performance in classification of characteristic waves.

• 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.

• Sleep Medicine and Psychophysiology
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• v.24 no.1
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• pp.24-31
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• 2017

Objectives: Periodic limb movement disorder (PLMD) has been debated with regard to its clinical significance and diagnostic criteria. The current diagnostic criterion for PLMD in adults has been changed from periodic limb movement index (PLMI) > 5/hour to PLMI > 15/hour by the International Classification of Sleep Disorders (ICSD). In this study, we aimed to investigate the changes in polysomnographic sleep variables according to PLMI and to determine the relevance of the diagnostic criterion for PLMD. Methods: Out of 4195 subjects who underwent standard polysomnography, we selected 666 subjects (370 males and 296 females, aged $47.1{\pm}14.8$) who were older than 17 years and were not diagnosed with primary insomnia, sleep apnea, narcolepsy, or REM sleep behavior disorder. Subjects were divided into three groups according to PLMI severity: group 1 ($PLMI{\leq}5$), group 2 (5 < $PLMI{\leq}15$), and group 3 (PLMI > 15). Demographic and polysomnographic sleep variables and Epworth sleepiness scale (ESS) were compared among the three groups. Results: There were significant differences among the three groups in age and gender. Sleep efficiency (SE) and stage 3 sleep percentage in group 1 were significantly higher than those in groups 2 and 3. The wake after sleep onset (WASO) score in group 1 was significantly lower than those in groups 2 and 3. However, there were no significant differences in SE, stage 3 sleep percentage, or WASO between groups 2 and 3. Sleep latency (SL) in group 1 was significantly lower than that in group 3, but there was no difference in SL between group 2 and group 3. ESS score in group 1 was significantly higher than that in group 3, but there was no difference between group 2 and group 3. Partial correlation analysis adjusted by age showed that PLMI was significantly related to SE and WASO. Conclusion: This study suggests that PLMI influences polysomnographic sleep variables. In addition, we found the individuals who did not have PLMD but had PLMI > 5 were not different in polysomnographic sleep variables from the individuals who had PLMD according to the current criterion. These results raise questions about the relevance of the current diagnostic criterion of PLMD.

• Journal of agricultural medicine and community health
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• v.38 no.2
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• pp.97-107
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• 2013

Objective: This study aimed to evaluate attention, memory and executive function in patients with narcolepsy. Methods: This study included 23 narcoleptic patients whose diagnosis were confirmed by the International Classification of Sleep Disorders(ICSD) at Chonnam National University Hospital Sleep Disorders Clinic or an other hospital in Korea, from 2005 to 2008, as well as 23 normal controls. All participants were given an IQ test for Korean-Wechsler Adult Intelligence Scale and several neuropsychological function tests (the d2 test for attention function, the Rey Complex Figure Test for nonverbal memory, the Korean-California Verbal Learning Test [K-CVLT] for verbal memory, and the Wisconsin Card Sorting Test for executive function). Clinical features of narcoleptic patients, including the frequency of excessive daytime sleepiness, cataplexy, sleep paralysis and hypnagogic hallucination, were investigated by a structured clinical interview administered by a neuropsychiatist. Excessive daytime sleepiness was evaluated by the Epworth sleepiness scale. Results: Characteristic symptoms of narcolepsy observed in this study included excessive daytime sleepiness (n=23, 100.0%), cataplexy (n=19, 82.6%), hypnagogic hallucination (n=5, 21.7%) and sleep paralysis (n=12, 52.2%). In nocturnal polysomnographic findings, stage 2 sleep and REM latency were found to be significantly decreased in narcoleptic patients compared with the control group, and were accompanied by significant increases in stage 1 sleep. Narcoleptic patients had lower scores than the control group on total number, Total Number-Total Error, Concentration Performance and Fluctuation Rate on the d2 test, which measures attention. Also, there were significant differences between the performance of patient and control groups on the B list of the K-CVLT, which measures verbal memory. Conclusion: Narcoleptic patients showed decreased attention and verbal memory performance compared to the control group; however, in many areas, narcoleptic patients still demonstrated normal cognitive function.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.633-646
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• 2020

We reviewed all studies assessing the health-related quality of life (HRQoL) in patients with hepatocellular carcinoma (HCC) between 2009 and 2018 (n = 45). Most studies assessed HRQoL as an outcome, and evaluated or compared the HRQoL of HCC patients depending on the type of treatment or stage of disease. HCC patients had a worse HRQoL than the general population, including in those with early-stage HCC. Patients commonly experienced pain, fatigue, sleep disturbance, distress, and lack of appetite, and these symptoms remained problematic even a few years after treatment. TNM classification of malignant tumors stage, tumor stage, presence of cirrhosis, being Asian, being female, living alone, or being unemployed were associated with a poor HRQoL. While recent studies have included a more diverse patient population, various topics, and different study designs, there were limited studies on supportive interventions. Given the increase in HCC cases and HCC survivors, addressing the HRQoL of HCC patients requires more attention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.