• Journal of Biomedical Engineering Research
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• v.34 no.2
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• pp.97-103
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• 2013

This paper proposes a new algorithm for sleep apnea detection based on stroke volume. It is very important to detect sleep apnea since it is a common and serious sleep-disordered breathing (SDB). In the previous studies, methods for sleep apnea detection using heart rate variability, airflow and blood oxygen saturation, tracheal sound have been proposed, but a method using stroke volume has not been studied. The proposed algorithm consists of detection of characteristic points in continuous blood pressure signal, estimation of stroke volume and detection of sleep apnea. To evaluate the performance of algorithm, the MIT-BIH Polysomnographic Database provided by Phsio- Net was used. As a result, the sensitivity of 85.99%, the specificity of 72.69%, and the accuracy of 84.34%, on the average were obtained. The proposed method showed comparable or higher performance compared with previous methods.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.17-23
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• 2017

In this paper, we implemented a respiration measurement system consisting of piezoelectric sensor, respiration signal processing device, and a viewer on a notebook. We tried an experiment for measuring respiration and detecting sleep apnea syndrome when a subject lay on a bed. We applied the respiration measurement algorithm to sensor data obtained from four subjects. In order to get a good graph shape, data manipulation methods such as moving averages and maximum values were applied. The window size for moving average was chosen as N=70, and the threshold value for each subject was customized. In this case, the proposed system showed 96.0% accuracy. When the maximum value among 90 data was applied instead of moving average, our system achieved 95.1% accuracy. In an experiment for detecting sleep apnea syndrome, the system showed that sleep apnea occurred correctly and calculated the average interval of sleep apnea. While infants or the elderly as well as patients with sleep apnea syndrome are lying down on a bed, our results are also expected to be able to cope with some accidental emergency situation by observing their respiration and detecting sleep apnea.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.119-129
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• 2023

Sleep apnea causes various secondary disease such as hypertension, stroke, myocardial infarction, depression and cognitive impairment. Early detection and continuous management of sleep apnea are urgently needed since it causes cardio-cerebrovascular diseases. In this study, wearable device for monitoring respiration during sleep using PVDF film was developed to detect vibration through trachea caused by breathing, which determines normal breathing and sleep apnea. Variables such as respiration rate and apnea were extracted based on the detected breathing sound data, and a noise reduction algorithm was established to minimize the effect even when there is a noise signal. In addition, it was confirmed that irregular breathing patterns can be analyzed by establishing a moving threshold algorithm. The results show that the accuracy of the respiratory rate from the developed device was 98.7% comparing with the polysomnogrphy result. Accuracy of detection for sleep apnea event was 92.6% and that of the sleep apnea duration was 94.0%. The results of this study will be of great help to the management of sleep disorders and confirmation of treatment by commercialization of wearable devices that can monitor sleep information easily and accurately at home during daily life and confirm the progress of treatment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.10
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• pp.802-809
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• 2017

This paper presents a novel apnea detection and respiration rate estimation method using impulse-radio ultra-wideband (IR-UWB) radar. The proposed method utilizes amplitude, time of arrival, and power in the selected band. The experimental results show that respiration rate can be estimated accurately using proposed method. And, it is shown that the selectivity between apnea and respiration can be improved more than 50 dB using the proposed method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.57-58
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• 2022

Sleep apnea (SA) is a common chronic sleep disorder that disrupts breathing during sleep. Clinically, the standard for diagnosing SA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of SA diagnoses in public health sectors. Therefore, ECG-based methods for SA detection have been proposed to automate the PSG procedure and reduce its discomfort. We propose a preprocessing method to convert the one-dimensional time series of ECG into two-dimensional images using the Gramian Angular Field (GAF) algorithm, extract temporal features, and use a two-dimensional convolutional neural network for classification. The results of this study demonstrated that the proposed method can perform SA detection with specificity, sensitivity, accuracy, and area under the curve (AUC) of 88.89%, 81.50%, 86.11%, and 0.85, respectively. Our experimental results show that SA is successfully classified by extracting preprocessing transforms with temporal features.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.464-467
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• 1997

In this study, ECG was recorded during sleep patients with obstructive sleep apnea. We detecte(heart rate variability) signal from the ECG wa QRS detection algorithm. And we observed HRV by the power spectrum density using autoregr modeling. The experimental results were analysis sleep stage 1, sleep stage 2, sleep stage 3, sleep s sleep stage REM. In experimental result, the PSD with obstructive sleep apnea patients was distributed low frequency band except sleep step 4. These effect means that the sympathetic nervous system affected the sleep stage 1, 2, REM and the parasympathetic nervous system affected the sleep stage 3, 4 with obstructive sleep apnea patients.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2879-2882
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• 2003

Obstructive Sleep Apnea(OSA) is a representative symptom of sleep disorder which is caused by airway obstruction. OSA is usually diagnosed through the laboratory based Polysomnography(PSG) which is uncomfortable and expensive. In this paper, the detection method for OSA events, using ECG, has been developed. The proposed method uses the ECG data sets provided from Physionet. The features for OSA events detection are the average and standard deviation of 1 minute R-R interval, power spectrum of R-R interval and S-pulse amplitude from data sets. These features are applied to the input of Neural Network. To evaluate the method, we used the another ECG data sets. And we achieved sensitivity of 89.66％, specificity of 95.25％. So, we can know that the features proposed in this paper are important to detect OSA.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.30 no.2
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• pp.150-156
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• 2004

The etiology of the obstructive sleep apnea includes the various factors such as anatomical abnormality in upper airway, craniofacial structure, obesity and personal habit. To establish reasonable treatment plan, multi-department approach is should be emphasized because the treatment modality is depend on the result of analysis for degree & site of obstruction and various behavioral factors. In Sleep Clinic in Keimyung University Medical Center, the standard of care for sleep apnea patient was established according to the Standard of practice committee of Americal Sleep Disorders Association. After one year experience of comprehensive approach for sleep apnea we could achieve following recommendation for the treatment. 1) The multi-department examination and diagnosis could prevent unnessesary treatment because the treatment plan could be established under comprehensive discussion. 2) Determination of the site of obstruction is important for treatment planning. However, no single determinant could be found. We expect multi-department approach can reduce the mistake in detection of obstruction. 3) Further evaluation of treatmet outcome should be succeeded to establish Korean standard of care for sleep apnea treatment.

• Sleep Medicine and Psychophysiology
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• v.3 no.2
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• pp.65-76
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• 1996

Natural sleep pattern and its physiology in childhood are much different from those in adulthood. Several aspects of clinical evaluation for sleepiness in childhood are more difficult than in adulthood. These difficulties are due to several factors. First, excessive sleepiness in childhood do not always develop functional impairments. Second, objective test such as MSLT may not be reliable since it is hard to be certain that the child understand instructions. Third, sleepiness in children is often obscured by irritability. paradoxical hyperactivity, or behavioral disturbances. Anseguently, careful clinical evaluation is needed for the sleepy children. Usual causes of sleepiness in children are the disorders that induce insufficient sleep such as sleep apnea syndrome, schedule disorder, underlying medical and psychiatric disorder, and so forth. After excluding such factors, we can diagnose the hypersomnic disorders such as narcolepsy, Kleine-Levin syndrome, and idiopathic central nervous system hypersomnia. Among the variety of those causes of sleepiness, I reviewed the clinical difference of narcolepsy and obstructive sleep apnea syndrome in childhood compared with in adulthood. Recognition of the childhood narcolepsy is difficult because even severely sleepy children often do not develop pathognomic cataplexy and associated REM phenomena until much later. Since childhood narcolepsy give srise to many psychological, academical problem. Practicers should be concerned about these aspects. Childhood obstructive sleep apnea syndrome is different from adult obstructive sleep apnea syndrome too. Several aspects such as pathophysiology. clinical feature, diagnostic criteria, complication, management, and prognosis differ from those in the adult syndrome. An important feature of childhood obstructive sleep apnea syndrome is the variety of severe complications such as behavioral disorders, cognitive impairment, cardiovascular symptoms, developmental delay, and ever death. Fortunately, surgical interventions like adenotosillectomy or UPPP are more effective for Childhood OSA than adult form. CPAP is a "safe, effective, and well-tolerated" treatment modality too. So if early detection and proper management of childhood OSA were done, the severe complication would be prevented or ever cured.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.90-95
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• 2006

Polysomonography, a way of diagnosing patients for Sleep Apnea, measures several kinds of bio-signal simultaneously. So it makes patients' behavior restricted and the examination more expensive. In this paper, presents another way of examining patients' ECGs, which are transferred to a computer system through a new wireless communication, Zigbee. This way of using Zigbee has solved restrictions of places and time for Polysomonography; and thus it is possible to reduce the cost, as well as improving patients' liberty.