• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.5
• /
• pp.171-180
• /
• 2023

The three major sleep disorders in Korea are snoring, sleep apnea, and insomnia. Lack of sleep is the root of all diseases. Some of the most serious potential problems associated with sleep deprivation are cardiovascular problems, cognitive impairment, obesity, diabetes, colitis, prostate cancer, etc. To solve these problems, the Korean government provided low-cost national health insurance benefits for polysomnography tests in July 2018. However, insomnia patients still have problems getting treated in terms of time, space, and economic perspectives. Therefore, it would be better for insomnia patients to be allowed to test at home. The measuring device can measure six biosignals (eye movement, tossing and turning, body temperature, oxygen saturation, heart rate, and audio). A gyroscope sensor (MPU9250, InvenSense, USA) was used for eye movement, tossing, and turning. The input range of the sensor was in 258°/sec to 460°/sec, and the data range was in the input range. Body temperature, oxygen saturation range, and heart rate were measured by a sensor (MAX30102, Analog Devices, USA). The body temperature was measured in 30 ℃ to 45 ℃, and the oxygen saturation range was 0% for the unused state and 20 % to 90 % for the used state. The heart rate measurement range was in 40 bpm to 180 bpm. The measurement of audio signal was performed by an audio sensor (AMM2742-T-R, PUIaudio, USA). The was -42 dB ±1 dB frequency range was 20 Hz to 20 kHz. The measured data was successfully received in wireless network conditions. The system configuration was consisted of a PC and a mobile app for bio-signal measurement and data collection. The measured data was collected by mobile phones and desktops. The data collected can be used as preliminary data to determine the stage of sleep and perform the screening function for sleep induction and sleep disturbances. In the future, this convenient sleep measurement device could be beneficial for treating insomnia.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.10
• /
• pp.207-216
• /
• 2013

The aim of this study is to design a classifier for sleep stage analysis and select important feature set which shows sleep stage well based on physiological signals during sleep. Sleep has a significant effect on the quality of human life. When people undergo lack of sleep or sleep-related disease, they are likely to reduced concentration and cognitive impairment affects, etc. Therefore, there are a lot of research to analyze sleep stage. In this study, after acquisition physiological signals during sleep, we do pre-processing such as filtering for extracting features. The features are used input for the new combination algorithm using genetic algorithm(GA) and neural networks(NN). The algorithm selects features which have high weights to classify sleep stage. As the result of this study, accuracy of the algorithm is up to 90.26% with electroencephalography(EEG) signal and electrocardiography(ECG) signal, and selecting features are alpha and delta frequency band power of EEG signal and standard deviation of all normal RR intervals(SDNN) of ECG signal. We checked the selected features are well shown that they have important information to classify sleep stage as doing repeating the algorithm. This research could use for not only diagnose disease related to sleep but also make a guideline of sleep stage analysis.

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

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.42 no.3 s.303
• /
• pp.47-52
• /
• 2005

Obstructive Sleep Apnea (OSA) is a representative symptom of sleep disorder caused by the obstruction of upper airway. Because OSA causes not only excessive daytime sleepiness and fatigue, hypertension and arrhythmia but also cardiac arrest and sudden death during sleep in the severe case, it is very important to detect the occurrence and the frequency of OSA. OSA is usually diagnosed through the laboratory-based Polysomnography (PSG) which is uncomfortable and expensive. Therefore researches to improve the disadvantages of PSG are needed and studies for the detection of OSA using only one or two parameters are being made as alternatives to PSG. In this paper, we developed an algorithm for the detection of OSA based on Heart Rate Variability (HRV). The proposed method is applied to the ECG data sets provided from PhysioNet which consist of learning set and training set. We extracted features for the detection of OSA such as average and standard deviation of 1 minute R-R interval, power spectrum of R-R interval and S-peak amplitude from data sets. These features are applied to the input of neural network. As a result, we obtained sensitivity of $89.66\%$ and specificity of $95.25\%$. It shows that the features suggested in this study are useful to detect OSA.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.905-910
• /
• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

• Broadcasting and Media Magazine
• /
• v.25 no.1
• /
• pp.67-74
• /
• 2020

사용자의 건강 및 인지 상태 모니터링을 위해 다양한 생체신호를 측정 및 분석하여 예측할 수 있다. 특히 최근 상용화되고 있는 웨어러블 센서 시스템을 이용하여 손쉽게 심전도나 액티그래피 움직임 정보를 사용자로부터 일상생활 중 장시간 얻어낼 수 있다. 그러나 사용자 상태 예측을 위한 기존 생체신호 분석 모델들은 생체신호 데이터의 성질을 최대한 반영하지 못하여, 본 논문에서는 최근 급속도로 발전하고 있는 인공지능 딥러닝 기술을 이용한 극복 방안에 대해 소개한다. 상태 모니터링의 구체적인 응용 예로 사용자 스트레스 및 수면 모니터링 분석에 생체신호 데이터 기반 딥러닝 기술을 적용하여 기존 모델보다 높은 성능을 보여주고 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.324-326
• /
• 2011

Measuring a biosignal during sleep is an important part of diagnosis and treatment of sleep disorder and also used to determine the general quality of sleep. As in current polysomnography, Contact method, which requires the attachment of electrodes to the skin, is the typical method to measure a biosignal during sleep. The procedure of this test is often considered to be inconvenient and tiresome because it requires attaching the device to the skin for each observation, and also limits free movement throughout the test. For this reason, the research on the acquiring the biosignal information without any attachment of a fixture on the skin is being conducted actively these days. In this study, it is suggested to check the heart rate per minute and the presence of breathing by placing a Piezo, which is a film type of pressure sensor, on the bed.

• Journal of the Korea Convergence Society
• /
• v.12 no.9
• /
• pp.31-37
• /
• 2021

Recently, in the market of home appliances, the technical differentiation of products using convergence technology has been receiving a lot of response to satisfy consumer demand. However, air-conditioner products are an area that requires research and development in the early stages of convergence technology. In this paper, it is developed that a non-contact bio-signal(respiration, movement) collection technology using IR-UWB(Impulse-Radio Ultra Wideband) technology, which controls the air-conditioner direction according to the user's location and also monitors sleep to provide an optimal sleep environment. In addition, emotional lighting and ASMR are developed to provide a comfortable and emotional place of life. Finally, based on the developed convergence technology, we develop intelligent smart air-conditioning services for the convenience of daily life and a comfortable resting space.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.630-632
• /
• 2023

어텐션 메커니즘은 딥 뉴럴네트워크에 결합하여 언어 생성 모델에서 성능을 개선하였고, 이러한 성공은 다양한 신호처리 분야에 응용 및 확장되고 있다. 특정 입력 신호 부분에 선택적으로 집중함으로써, 어텐션 모델은 음성 인식, 이미지와 비디오 처리, 그리고 생체인식 등의 분야에서 더 높은 성능을 보여주고 있다. 어텐션 기반 모델은 심전도 신호를 이용한 개인식별 및 부정맥검출, 뇌파도 신호를 이용한 발작유형분류 및 수면 단계 분류, 근전도 신호를 이용한 제스처 인식 등에 사용되고 있다. 어텐션 메커니즘은 딥 뉴럴네트워크의 해석 가능성과 설명 가능성을 향상시키기 위해 사용되기도 한다. 신호 처리 분야에서의 어텐션 모델 연구는 지속적으로 진행 중이며, 다른 분야에서의 잠재력 탐구에 대한 관심이 높아지고 있다. 따라서 본 논문은 어텐션 메카니즘을 이용한 생체신호처리 연구 동향 분석을 수행한다.

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