• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.5-15
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• 2006

In this paper, we measured the biosignal using hot-water system(STYX ford202) on foot. The biosignal transition data is observed from hot-water thermotherapy on foot. pre and post demonstration conditions under 43$^{\circ}C$ and 45$^{\circ}C$ are checked about 9 physiological factors for 10 persons and 10 days. (Checking Time: pre-test, post-test(5, 10, 15, 20 minutes)). The biosignal transition of demonstration's results showed as belows; Forehead Temperature($^{\circ}C$): -0.69 $\pm$ 0.01 dec, Leg Temperature($^{\circ}C$): 1.51 $\pm$ 0.22 inc, Blood Flow($m\ell/min$): 1.18 $\pm$ 0.50 inc, Blood Pressure(mmHg): (max) -1.49$\pm$ 2.81, (min) -0.06 $\pm$ 0.13 dec, Heart Rate(bpm): 6.97 $\pm$ 0.72 inc, Blood Sugar($mg/d\ell$) : -2.41 $\pm$ 1.55 dec, Oxygen Saturation(%): 1.34 $\pm$ 0.28 inc, Body Fat(%) -1.75 $\pm$ 0.15 dec, Weight(kg): -0.10 $\pm$ 0.04 dec. (dec: decrease, inc: increase)

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.119-138
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• 1999

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.501-506
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• 2008

Medical environments incorporate complex and integrated data networks to transfer vast amounts of patient information, such as images, waveforms, and other digital data. To assure interoperability of images, waveforms and patient data, health level seven(HL7) was developed as an international standard to facilitate the communication and storage of medical data. We also adopted medical waveform description format encoding rule(MFER) standard for encoding waveform biosignal such as ECG, EEG and so on. And, the study converted a broad domain of clinical data on patients, including MFER, into a HL7 message, and saved them in a clinical database in hospital. According to results obtained in the test environment, it was possible to acquire the same HL7 message and biosignal data as ones acquired during transmission. Through this study, we might conclude that the proposed system can be a promising model for electronic medical record system in u-healthcare environment.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.12-18
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• 2004

본 글에서는 인체로부터 생체전기신호를 측정하고 처리 및 해석하는 기술을 소개한다. 일반적인 계측 시스템을 구성하는 필수적인 네 가지 요소는 측정대상, 센서부, 신호처리부, 그리고 출력부이다. 생체전기신호의 측정에서 측정대상은 인체를 포함하는 생명체이다. 경우에 따라서는 생명체로부터 떼어 낸 특정 부위가 측정대상이 될 수도 있으나 본 글에서는 살아 있는 인체를 측정대상으로 설정하기로 한다. 또한 인체로부터 방사되는 에너지를 측정하는 비접촉 방식은 다루지 않고, 측정 부위를 인체의 내부 또는 표면으로 제한한다. 즉, 센서를 측정 부위에 직접 부착하는 접촉형 인체-센서 인터페이스 방법을 사용하는 경우만을 다루기로 한다.(중략)

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.37-42
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• 2010

Final year students are normally encountering high pressing in their study. In view of this fact, this research focuses on determining mental states condition of college student in final year based on the psycho-physiological information. The experiments were conducted in two times, i.e., prior- and post- graduation seminar examination. The early results indicated that the student profile of mood states (POMS) in prior final graduation seminar showed higher scores than students in post final graduation seminar. Thus, in this research, relation between biosignal representing by heart rate variability (HRV) and questionnaire responses were evaluated by hidden Markov model (HMM) and neural networks (NN).

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1186-1191
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• 2007

Human driver monitoring system is one of the most important systems for the safety in driving vehicles, and therefore driver assistance system has gained much attention during the last decade. This paper proposed an intelligent driver assistance system which monitors human driver's states from bio-signals such as ECG and Blood Pressure. The proposed system used mamdani fuzzy inference to evaluate the driver's mental strain and generated warning signals to the driver. The approach using bio-signals in driver assistance system is the main issue of the related systems and the preliminary results showed the possibility of further research topics in developing more intelligent embedded systems with bio-signal feedback.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.313-321
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• 2021

The global market for medical devices is huge, and it will continue showing remarkable growth in the future. However, due to the entry barrier to develop medical devices, many domestic companies have technical problems in entering the medical device industry. In this paper, we introduce an open platform that can help with research and development for companies in the healthcare industry. This open platform consists of a hardware part and a software part. A hardware part is combined into CPU, base and other modules that are easy to replace and assemble. A software part is based on application software for development developed by Bionet. We test the performance of the open medical device platform using a biosignal processing algorithm.

• Proceedings of the Optical Society of Korea Conference
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• 2004.07a
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• pp.294-295
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• 2004

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