• Journal of Biomedical Engineering Research
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• v.38 no.4
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• pp.153-162
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• 2017

The present study proposes a system that can detect sleep-disordered breathing automatically using an air mattress and oxygen saturation. A thin air mattress was fabricated to reduce discomfort during sleep, and respiration signals were acquired. The system was configured to be synchronized with a polysomnography to receive signals simultaneously with other bio-signals. The present study has been conducted with nine adult male and female patients with sleep-disordered breathing, and sleep-disordered breathing events have been detected by applying the signals acquired from the subjects to the rule-based detection algorithm. The sensitivity and positive predictive values were found to evaluate the performance of the system, which are 91.4% and 89.7% for all events, respectively. The comparison of apnea hypopnea index(AHI) between the polysomnography and the proposed method showed squared R-value of 0.9. This study presents the possibility of detecting sleep-disordered breathing at hospitals or homes using the proposed system.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.1-5
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• 2021

Sleep apnea refers to a condition in which a person does not breathe during sleep, and is a dangerous symptom that blocks oxygen supply in the body, causing various complications, and the elderly and infants can die if severe. In this paper, we present an algorithm that classifies sleep breathing by analyzing the intensity of breathing with images alone in preparation for the risk of sleep apnea. Only the chest of the person being measured is set to the Region of Interest (ROI) to determine the breathing strength by the differential image within the corresponding ROI area. The adult was selected as the target of the measurement and the breathing strength was measured accurately, and the difference in breathing intensity was also distinguished using depth information. Two videos of sleeping babies also show that even microscopic breathing motions smaller than adults can be detected, which is also expected to help prevent infant death syndrome (SIDS).

• Journal of Sensor Science and Technology
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• v.29 no.4
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• pp.249-254
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• 2020

It is difficult to control children who exhibit negative behavior in dental clinics. Various methods are used for preventing pediatric dental patients from being afraid and for eliminating the factors that cause psychological anxiety. However, when it is difficult to apply this routine behavioral control technique, sedation therapy is used to provide quality treatment. When the sleep anesthesia treatment is performed at the dentist's clinic, it is challenging to identify emergencies using the current breath detection method. When a dentist treats a patient that is under the influence of an anesthetic, the patient is unconscious and cannot immediately respond, even if the airway is blocked, which can cause unstable breathing or even death in severe cases. During emergencies, respiratory instability is not easily detected with first aid using conventional methods owing to time lag or noise from medical devices. Therefore, abnormal breathing needs to be evaluated in real-time using an intuitive method. In this paper, we propose a method for identifying abnormal breathing in real-time using an intuitive method. Respiration signals were measured using a 3M Littman electronic stethoscope when the patient's posture was supine. The characteristics of the signals were analyzed by applying the signal processing theory to distinguish abnormal breathing from normal breathing. By applying a short-time Fourier transform to the respiratory signals, the frequency range for each patient was found to be different, and the frequency of abnormal breathing was distributed across a broader range than that of normal breathing. From the wavelet transform, time-frequency information could be identified simultaneously, and the change in the amplitude with the time could also be determined. When the difference between the amplitude of normal breathing and abnormal breathing in the time domain was very large, abnormal breathing could be identified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5527-5545
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• 2019

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.309-322
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• 2014

Detection of fatigue cracks at an early stage of their development is important in structural health monitoring. The breathing of cracks in a structure generates higher harmonic components of the exciting frequency in the frequency spectrum. Previously, the residual operational deflection shape (R-ODS) method was successfully applied to beams with a single crack. The method is based on the ODSs at the exciting frequency and its higher harmonic components which consider both amplitude and phase information of responses to map the deflection pattern of structures. Although the R-ODS method shows the location of a single crack clearly, its identification for the location of multiple cracks in a structure is not always obvious. Therefore, an improvement to the R-ODS method is presented here to make the identification process distinct for the beams with multiple cracks. Numerical and experimental examples are utilised to investigate the effectiveness of the improved method.

• Journal of the Korea Safety Management & Science
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• v.21 no.4
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• pp.1-6
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• 2019

This study relates to acquiring biological signal without attaching directly to the user using UWB(Ultra Wide Band) radar. The collected information is the respiratory rate, heart rate, and the degree of movement during sleep, and this information is used to measure the sleep state. A breathing measurement algorithm and a sleep state detection algorithm were developed to graph the measured data. Information about the sleep state will be used as a personalized diagnosis by connecting with the medical institution and contribute to the prevention of sleep related diseases. In addition, biological signal will be linked to various sensors in the era of the 4th industrial revolution, leading to smart healthcare, which will make human life more enriching.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.36-41
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• 2022

In the 21st century, information collection and information provision based on digital informatization and intelligent automation are emerging as one of the social problems in the society for the elderly and the vulnerable groups in the welfare society including the disabled, and various methods are being studied to find realistic alternatives. Among these factors, the problem of the elderly living alone is emerging as the most serious, and as a realistic approach to solve some problems by applying information devices, it is a monitoring system using the Internet of Things(IoT). The need for an optimized system is emerging. In this study, the state of the elderly and the elderly living alone can be measured remotely by applying IoT technology. We present the research cases of a Breathing Sensor-based Smart Monitoring System that is used as a smart information system and used as a monitoring system for the elderly and infirm when it is identified as deceased through state detection

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.514-517
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• 2003

Oxygen supply is one of the most basic things in human body. Breathing is controlled by the lungs' stationary function and the respiratory center which is in the mesulla oblongata. Nothing but, the external breathing that air movement between the lungs and atmosphere and the internal breathing that cellular air movement between the hemoglobin and a single cell. The adult's number of times of the respirations is about 15∼20 per 1 minute, but it depends ages, exercise, temperature, disease, etc. The important thing in detecting the respiration is that doing it in object person's resting time. Detecting the respiration have to be done without attracting any attention of object person. In present using method is detecting the pulse with catching an object person's wrist and observing the object person's movement. In this paper, we propose the mobile respiration detection diagnostic system using ultrasound sensing method that does not be influenced by the inertia error and the pressure error.

• Investigative Magnetic Resonance Imaging
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• v.15 no.1
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• pp.22-31
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• 2011

Purpose : To compare free-breathing and respiratory-triggered diffusion-weighted imaging on 1.5-T MR system in the detection of hepatic lesions. Materials and Methods: This single-institution study was approved by our institutional review board. Forty-seven patients (mean 57.9 year; M:F = 25:22) underwent hepatic MR imaging on 1.5-T MR system using both free-breathing and respiratory-triggered diffusion-weighted imaging (DWI) at a single examination. Two radiologists retrospectively reviewed respiratory-triggered and free-breathing sets (B50, B400, B800 diffusion weighted images and ADC map) in random order with a time interval of 2 weeks. Liver SNR and lesion-to-liver CNR of DWI were calculated measuring ROI. Results : Total of 62 lesions (53 benign, 9 malignant) that included 32 cysts, 13 hemangiomas, 7 hepatocellular carcinomas (HCCs), 5 eosinophilic infiltration, 2 metastases, 1 eosinophilic abscess, focal nodular hyperplasia, and pseudolipoma of Glisson's capsule were reviewed by two reviewers. Though not reaching statistical significance, the overall lesion sensitivities were increased in respiratory-triggered DWI [reviewer1: reviewer2, 47/62(75.81%):45/62(72.58%)] than free-breathing DWI [44/62(70.97%):41/62(66.13%)]. Especially for smaller than 1 cm hepatic lesions, sensitivity of respiratory-triggered DWI [24/30(80%):21/30(70%)] was superior to free-breathing DWI [17/30(56.7%):15/30(50%)]. The diagnostic accuracy measuring the area under the ROC curve (Az value) of free-breathing and respiratory-triggered DWI was not statistically different. Liver SNR and lesion-to-liver CNR of respiratory-triggered DWI ($87.6{\pm}41.4$, $41.2{\pm}62.5$) were higher than free-breathing DWI ($38.8:{\pm}13.6$, $24.8{\pm}36.8$) (p value < 0.001, respectively). Conclusion: Respiratory-triggered diffusion-weighted MR imaging seemed to be better than free-breathing diffusion-weighted MR imaging on 1.5-T MR system for the detection of smaller than 1 cm lesions by providing high SNR and CNR.

• 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.