• 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.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.173-175
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• 2023

Enuresis nocturia is more prevalent in children than in adults. Using polysomnography (PSG), we investigated the causes of adult enuresis nocturia in a 20-year-old female patient with nighttime bedwetting. In spite of normal urological examinations, her detailed medical history disclosed frequent sleep paralysis and urination during dreams. During PSG, two electromyograms were attached to her anus to assess the tone of her bladder's sphincter while she slept. During REM sleep, the EMG tone of the mandible decreased, but the anal and bladder sphincter tones did not. The polysomnogram revealed moderate obstructive sleep apnea. Consequently, she was diagnosed with adult parasomnia (nocturnal enuresis) overlap syndrome with OSA. This study demonstrates the value of PSG with simultaneous anal tone EMG for diagnosing NREM parasomnia and nocturnal enuresis.

• The Journal of the Korean dental association
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• v.53 no.4
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• pp.238-248
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• 2015

Sleep related breathing disorders(SRBDs) are a group of diseases accompanied by difficulties in respiration and ventilation during sleep. Central sleep apnea, obstructive sleep apnea(OSA), sleep-related hypoventilation, and hypoxemia disorder are included in this disease entity. OSA is known to be the most common SRBDs and studies show its significant correlation with general health problems including hypertension, arrhythmia, diabetes, and metabolic syndrome. The diagnostic process of OSA is composed of physical examinations of the head and neck area and also the oral cavity. Radiologic studies including cephalography, CT, MRI, and fluoroscopy assist in identifying the site of obstruction. However, polysomnography(PSG) is still considered the gold standard for the diagnosis of OSA since it offers both qualitative and quantitative recording of the events during a whole night's sleep. The dentist who is trained in sleep medicine can easily identify patients with the risk of OSA starting from simple questions and screening questionnaires. Diagnosis is the first step to treatment and considering the high rate of under-diagnosis for OSA the dentist may play a substantial role in the diagnosis and treatment of OSA which will eventually lead to the well-being of the patient as a whole person. So the objective of this article is to assist dental professionals in gaining knowledge and insight of the diagnostic measures for OSA including PSG.

• Journal of Korean Living Environment System
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• v.19 no.4
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• pp.479-488
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• 2012

This study investigated the influence of the indoor CO₂concentration level on sleep quality by polysomnography(PSG). One healthy female subject was selected among several subjects based on RI(Risk Indicator) value and BMI(Body Mass Index) value to evaluate judging the risk level of obstructive sleep apnea hypopnea. To get the impact of the indoor carbon dioxide concentration to sleep quality, both CO₂concentration levels were set up using ventilating form with 700~800 ppm and 2000~3000 ppm. Other environments were controlled in the comfortable sleep scope by previous researches. To measure the sleep quality, measurements have carried on polysomnography(PSG). In conclusion, it have shown that high carbon dioxide concentration leads arousal effect about central nervous system and to sustaining dreams and excited condition by bring about REM sleep split phenomenon.

• Journal of Society of Preventive Korean Medicine
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• v.28 no.1
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• pp.109-118
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• 2024

Objectives : This study aimed to investigate the relationship between cold feet and sleep quality using polysomnography (PSG) and Pittsburgh Sleep Quality Index (PSQI). Methods : We divided 11 adults (6 females, 5 males) with Insomnia Severity Index score below 21 into cold feet (CF) and a non-cold feet (NCF) group based on the median feet temperature (Taichong, LR3). PSG and PSQI were administered to assess sleep characteristics and subjective sleep quality. Results : CF group exhibited significantly lower time in bed, sleep period time, and total sleep time compared to NCF group. While there were no significant group differences in sleep latency, wakefulness after sleep onset, or total arousal index, NCF group had significantly lower minimum oxygen saturation and apnea-hypopnea index in REM (rapid eye movement) sleep compared to CF group. Although the PSQI score and the proportion of poor sleepers were both higher in the CF group (7.40 and 80%) compared to the NCF group (5.50 and 50%), these differences did not reach statistical significance. Conclusions : This study showed that foot temperature affects sleep characteristics and suggests the need to utilize PSG in sleep research in Korean medicine.

• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.55-61
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• 2014

In sleep monitoring system, polysomnography (PSG) is the gold-standard but previous studies revealed that attaching numerous amount of sensors disturb sleep during the test which is the fundamental disadvantage of PSG. We suggest an unconstrained rapid-eye-movement (REM) sleep monitoring method measured with polyvinylidene (PVDF) film-based sensor for the normal and the obstructive sleep apnea (OSA) patients. Nine normal subjects and seventeen OSA patients have participated in the study. During REM sleep, rate and variability of respiration are known to be greater than in other sleep stages. Based on this phenomena, respiratory signals of participants were unconstrainedly measured using the PVDF-based sensor with the PSG and REM sleep were extracted from the average rate and variability of respiration. In epoch-by-epoch REM sleep detection, proposed method classified REM sleep with an average sensitivity of 72.3%, specificity of 92.5%, accuracy of 88.9%, and kappa statistic of 0.60 compared to the results of PSG. Student's t-test showed no significant difference between the results of normal and OSA group. This method is potentially applicable to REM sleep detection in homing environment or ambulatory monitoring.

• Sleep Medicine and Psychophysiology
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• v.3 no.1
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• pp.31-37
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• 1996

Uvulopalatopharyngoplasty(UPPP) is an operation that is frequently performed for the patient of obstructive sleep apnea(OSA). A major problem has been to select those patients who will have a good response to UPPP. We compared preoperative and postoperative polysomnography(PSG) in 20 patients to evaluate the success rate of the operation. Each subject underwent a cephalometric roentgenogram, and fiberoptic nasopharyngoscopy with Mueller maneuver was applied in roentgenogram and fiberoptic nasopharyngoscopy with Mueller maneuver was applied in preop evaluation of patients with OSA. No PSG parameter could accurately predict the changes in sleep after UPPP. There were no significant differences between the responders and the nonresponders concerning the cephalometric analysis, the type of obstruction by Mueller maneuver, and body mass index(BMI). The conclusions of this study are thus that UPPP is an effective treatment for the OSAS with a high success rate, but that there is no single useful parameter predicting the success of the operation.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.113-123
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• 2020

Obstructive sleep apnea (OSA) among sleep disorders is one of relatively common diseases. Patients can be checked for the disease through sleep polysomnography. However, as far as he diagnosis of OSA using polysomnography (PSG) is concerned, many practical problems such as an increasing number of patients, expensive testing cost, discomfort during examination, and the limited number of people for testing have been pointed out. Accordingly, for the purpose of substituting PSG researchers have been actively conducting studies on OSA diagnosis based on machine learning using bio signals. In this regard, we review a rich body of existing OSA diagnosis studies applying machine learning techniques based on bio-signal data. As a result, this paper presents a novel taxonomy of the reviewed studies and provides their comprehensive comparative analysis results. Also, we reveal various limitations of the studies using the bio signals and suggest several improvements about utilization of the used machine learning methods. Finally, this paper presents future research topics related to the application of machine learning techniques using bio signals.

• 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.

• Annals of Clinical Neurophysiology
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• v.17 no.1
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• pp.17-23
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• 2015

Background: Sleep-related disturbances and sleep disorders are common in Parkinson's disease (PD) and have a great impact on daily life of PD patients. This study was done to find the sleep characteristics and sleep disturbing factors in PD patients according to disease severity through clinical interview and polysomnographic (PSG) study. Methods: Fifty patients with PD (22 males, age $60.6{\pm}6.4$, Hoehn and Yahr (HY) stage $2.7{\pm}1.0$) were recruited and thoroughly interviewed about their sleep. PSG was performed on the patients taking routine antiparkinsonian medications. Patients were grouped into mild and moderate/severe group according to HY stage, and the results were compared between each group. Results: Ninety-four percent of total patients had one or more sleep-related disturbances based on the interview or PSG. On interview, the moderate/severe group complained more insomnia and REM sleep behavior disorder (RBD) than mild group. In PSG findings, the moderate/severe group showed lower sleep efficiency, longer sleep latency, REM sleep latency, waking time after sleep onset, and higher prevalence of RBD. Conclusions: In this study, most patients with PD had sleep disturbances. Clinical interview and PSG findings revealed deterioration of sleep quality along the disease severity. Our results suggest that sleep disturbances in PD patients are prevalent and warrant clinical attention, especially to the patients with advanced disease.