• 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.

• 전자공학회논문지 IE
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• v.49 no.2
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• pp.58-64
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• 2012

In this paper, we proposed a distributed gateway for ubiquitous healthcare system. We also designed and implemented protocol conversion and processing algorithms to exchange a seamless information, the bio signals between the databases and the receiving devices from ZigBee to gateway and from the gateway to database and network. The distributed gateway system consists of the bio signal acquisition, ZigBee modules, distributed databases, and gateways. The bio signals detected by the ZigBee module are sent to the gateway. The distributed gateway analyzes the data being transferred, sends those to the receiving devices, and lets the authorized personnel access. The proposed system can be utilized in various fields including activity analysis for the elderly, security systems, home network service, and so on.

• Journal of Oriental Neuropsychiatry
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• v.31 no.2
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• pp.89-99
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• 2020

Objectives: The purpose of this study was to identify the needs of bio-signal devices for the diagnosis, assessment, and analysis of neurocognitive disorder in Korean medicine (KM) hospitals and clinics. Methods: A questionnaire was developed to survey the current status of medical device use, and diagnosis and interventions for patients with cognitive disorders in KM hospitals and clinics. November 11~December 2, 2019, 114 responses (71.9% completed) were collected by internet-based questionnaires from the members of the Korean society of Oriental Neuropsychiatry. Results: The clinical requests were in the descending order of hematology analyzer, ultrasound imaging system, and electroencephalography among the 15 most commonly used devices of which research would support for their clinical usability. The biosignal-based devices showed the highest research demands for patients with mild cognitive impairment rather than more severe stages of cognitive impairment. Prevention rather than diagnosis, or several treatment regimens was the strongest clinical area of the KM for patients with neurodegenerative cognitive impairment. Many responded that five to 10 minutes of test duration and 20,000 won to 30,000 won of cost would be appropriated for a new device to be developed. Conclusions: There were strong demands for the development of bio-signal devices for neurocognitive disorders among the KM doctors. Specifically, it showed high needs for the technology that can be used in the prevention area of cognitive disorders. Additionally, new medical devices to assess cognitive functions and to obtain KM pattern-related information were the high needs.

• Progress in Medical Physics
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• v.3 no.1
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• pp.71-78
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• 1992

This study describes the design of the biophysiological signal processing analyzer which can collect and analyze the biosignal raw data. System hardware is consisted of the IBM PC AT. pre-amplifier. AID converter, Counter/Timer. and RS-232C processor. Biophysiological signal data were processed by the software digital filter. FFT and graphic processing routine. The tachogram and FFT of the the peak to peak interval time was accomplished by the Graphic user interface software using the biophysiological signal processed data. Using this system. the powerspectrum of the heart rate variability during the long term could be observed. Experimental results of this system approach our purpose. which is improved the cost performance. easy to use. reducing raw-data noise and optimizing model for digital filter.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.97-106
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• 2012

Management of biosignal data in mobile devices causes many problems in real-time transmission of large volume of multimedia data or storage devices. Therefore, this research paper intends to suggest an m-Health system, a clinical data processing system using mobile in order to provide quick medical service. This system deployed health system on IP network, compounded outputs from many bio sensing in remote sites and performed integrated data processing electronically on various bio sensors. The m-health system measures and monitors various biosignals and sends them to data servers of remote hospitals. It is an Android-based mobile application which patients and their family and medical staff can use anywhere anytime. Medical staff access patient data from hospital data servers and provide feedback on medical diagnosis and prescription to patients or users. Video stream for patient monitoring uses a scalable transcoding technique to decides data size appropriate for network traffic and sends video stream, remarkably reducing loads of mobile systems and networks.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.103-109
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• 2017

In order to apply the patient monitoring system to the hospital field, it is necessary to be able to measure and analysis data the major bio-signals that are basically covered by the existing patient monitoring system. We have implemented a wearable device and the patient monitoring system for measuring ECG and oxygen saturation. The implemented system transmits the measured bio-signal to the server on the nursing station via Bluetooth. It is represented by graph waveforms and numerical values that can be checked by the medical staff in the patient monitoring system. The validity of this system is verified by comparing the data collected through the designed system with the data obtained from the conventional equipment.

• BMB Reports
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• v.41 no.6
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• pp.415-434
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• 2008

Phosphoinositide-specific phospholipase C is an effector molecule in the signal transduction process. It generates two second messengers, inositol-1,4,5-trisphosphate and diacylglycerol from phosphatidylinositol 4,5-bisphosphate. Currently, thirteen mammal PLC isozymes have been identified, and they are divided into six groups: PLC-$\beta$, -$\gamma$, -$\delta$, -$\varepsilon$, -$\zeta$ and -$\eta$. Sequence analysis studies demonstrated that each isozyme has more than one alternative splicing variant. PLC isozymes contain the X and Y domains that are responsible for catalytic activity. Several other domains including the PH domain, the C2 domain and EF hand motifs are involved in various biological functions of PLC isozymes as signaling proteins. The distribution of PLC isozymes is tissue and organ specific. Recent studies on isolated cells and knockout mice depleted of PLC isozymes have revealed their distinct phenotypes. Given the specificity in distribution and cellular localization, it is clear that each PLC isozyme bears a unique function in the modulation of physiological responses. In this review, we discuss the structural organization, enzymatic properties and molecular diversity of PLC splicing variants and study functional and physiological roles of each isozyme.

• Fashion & Textile Research Journal
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• v.18 no.6
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• pp.755-765
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• 2016

Recently, interest in monitoring health and sports is growing because of the emphasis on wellness, which is accelerating the development and commercialization of smart clothing for biosignal monitoring. In addition to exerciseeffect monitoring clothing that tracks heart rate and respiration, recently developed clothing makes it possible to monitor muscle balance using electromyogram (EMG). The electrode for EMG have to attached to an accurate location in order to obtain high-quality signals in surface EMG measurement. Therefore, this study develops monitoring clothing suitable for different types of human bodies and aims to extract suitable range of EMG according to movements in order to develop self-fitness monitoring clothing based on EMG measurement. This study identified and attached electrodes on six upper muscles and two lower muscles of ten males in their 20s. After selecting six main motions that create a load on muscles, the 8-ch wireless EMG system was used to measure amplitude value, noise, SNR and SNR (dB) in each part and statistical analysis was conducted using SPSS 20.0. As a result, the suitable range for EMG measurement to apply to clothing was identified as four parts in musculus pectoralis major; three parts in muscle rectus abdominis, two parts each in shoulder muscles, backbone erector, biceps brachii, triceps brachii, and musculus biceps femoris; and four part in quadriceps muscle of thigh. This was depicted diagrammatically on clothing, and the EMG-monitoring sensing locations were presented for development of self-fitness monitoring.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.67-78
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• 2011

This paper analyzes the characteristics of drivers' workload observed from with 30 participant drivers with respect to two physio-physiological parameters. For investigating physio-physiological characteristics of road drivers, bio-signals from brain's occipital lobe between simulation experiment and real driving experiment are collected and analyzed. The major findings from the analysis are summarized as follows: First, the drivers' physio-physiological workload is a good parameter for explaining the workload characteristics of road drivers. Secondly, the two physio-physiological workload parameters selected, i.e., beta value and relative energy parameter, are revealed to be statistically significant. Thirdly, it is also revealed to be statistically significant to select 90 percentile measurements in simulator experiment to explain the road drivers' characteristics. Finally, the maximum workload of road design driver is 31.72 in beta parameter, whereas the minimum workload is 1.296 in relative energy parameter.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.381-391
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• 2017

Biometric technology is a technology for authenticating a user using the physical or behavioral features of the inherent characteristics of the individual. With the necessity and efficiency of the technology in the fields of finance, security, access control, medical welfare, inspection, and entertainment, the service range has been expanding. Biometrics using biometric information such as fingerprints and faces have been exposed to counterfeit and disguised threats and become a social problem. Recent studies using a bio-signal from the inside of the body other than the bio-information of the external body are being developed. This paper analyzes the recent research and technology of biometric systems using bio-signals, ECG, heart sounds, EEG, and EMG to present the skills needed for the development direction. In the future, utilizing the deep learning to build and analyze database to manage bio-signal based big data for the complex condition of individuals, biometrics technologies suitable for real time environment are expected to be researched.