• 대한의용생체공학회:의공학회지
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• 제38권4호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.

• 반도체디스플레이기술학회지
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• 제20권3호
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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).

• 센서학회지
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• 제29권4호
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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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• 제13권11호
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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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• 제1권3호
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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.

• 대한안전경영과학회지
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• 제21권4호
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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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• 제11권3호
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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

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 춘계종합학술대회
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• pp.514-517
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• 2003

산소공급은 신체 요구 중 가장 기본적인 것이다. 호흡은 뇌의 연수(medulla oblongata)에 있는 호흡중추와 폐의 정상적 기능에 의해 조절된다. 즉 폐와 환경 사이의 공기 이동인 외 호흡과 헤모글로빈과 단세포 사이의 세포수준에서의 산소 이동인 내 호흡을 말한다. 성인의 호흡수는 보통 1분에 15-20회이나 연령, 운동, 기온, 심리적 변호, 질병상태, 대기의 산소 함량, 약물 투여 등에 따라 차이가 난다. 호흡측정은 대상자가 쉬고 있을 때 하는 것이 중요하다. 호흡 측정은 측정하고 있다는 사실을 대상자가 모르도록 기술적으로 해야한다. 현재 사용하는 방법은 주의를 끌지 않도록 대상자의 팔목에 손을 댄 채로 맥박을 측정한 바로 직후 계속해서 대상자의 가슴의 움직임을 관찰하면서 호흡을 측정하는 것이다. 본 논문에서 구현하고자 하는 것은 관성의 오차 및 압력의 오차에 영향을 거의 받지 않는, 그리고 반영구적으로 사용이 가능한 초음파 센서를 이용한 임베디드 환경의 호흡 량 측정기이다.

• Investigative Magnetic Resonance Imaging
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• 제15권1호
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• pp.22-31
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• 2011

목적: 이 연구의 목적은 간 병변 발견에 있어 1.5-T 자기공명영상에서 자유 호흡 확산강조 영상과 호흡 유발 확산강조 영상의 유용성을 비교하는데 있다. 대상 및 방법: 47명의 환자(평균 57.9세, 남성:여성 = 25:22)가 한번의 간 자기공명 영상검사에서 자유호흡 확산 강조 영상과 호흡유발 확산 강조 영상을 동시에 시행하였다. 이를 두 명의 영상의학과 의사가 호흡유발 이미지 세트(B50, B400, B800 확산강조 영상과 ADC map)와 자유호흡 이미지 세트를 2주간의 시간 간격을 두고 무작위로 후향적 분석을 시행하였다. 영상분석을 위하여 특정영역(ROI)를 설정한 후에 간의 신호대 잡음비 (signal-to-noise ratio, SNR)와 대조도(contrast-to-noise ratio, CNR)를 계산하였다. 결과: 32개의 낭종, 13개의 혈관종, 7개의 간세포암, 6국소 호산구성 간질환, 2개의 전이, 1개의 초점성 결절성 과증식과 글리슨막의 가성지방종을 포함하는 총 62개의 병변이 두 명의 평가자에 의하여 분석되었다. 비록 통계적 유의성을 없었으나, 전체적인 병변 발견의 sensitivity는 호흡유발 확산강조 영상이 [평가자 1:평가자 2, 47/62(75.81%):45/62(72.58%)] 자유호흡 확산강조 영상보다 [44/62(70.97%):41/62(66.13%)] 더 높은 수치를 보였다. 특히 1 cm보다 작은 국소 간 병변 발견의 sensitivity는 호흡유발 확산강조 영상이 [24/30(80%): 21/30(70%)] 지유호흡 확산강조 영상보다 [17/30(56.7%):15/30(50%)] 더 우월하였다. 진단적 정확도활 계산하기 위하여 ROC curve (Az value)를 구하였으며 자유호흡 확산강조 영상과 호흡유발 확산강조 영상간에는 통계적 차이는 없었다. 간의 신호대 잡음비 (SNR)와 대조도 (CNR)는 호흡유발 확산강조 영상이 ($87.6{\pm}41.4$, $41.2{\pm}62.5$) 자유호흡확산강조 영상보다 ($38.8:{\pm}13.6$, $24.8{\pm}36.8$) 높았으며 통계적인 유의성이 있었다. (p value < 0.001). 결론: 1.5-T자기공명 시스템서 1 cm보다 작은 간 병변발견에 있어서 호흡유발 확산강조 영상이 자유호흡 확산강조 영상보다 좋으며 이는 호흡유발 확산강조 영상이 높은 신호대 잡음비 (SNR)와 대조도(CNR)를 보이기 때문이다.