• Journal of Biomedical Engineering Research
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• v.7 no.1
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• pp.67-74
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• 1986

We have implemented data compression and reconstruction by using a fast Walsh transform. The ECG signals were generated by an ECG BimLllator (KONT- RON). The sampling frequency was 480 Hz and the data point number used was 512. In order to eliminate the 60 Hz noise and baseline drift, a digital notch filter was designed. We obtaine!1 a compression ratio of 5 : 1 and at this ratio it was possible to obtain a true diagnosis and an ECG morphology analysis.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.265-268
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• 1995

Signal averaging technique to improve signal-to-noise ratio has widely been used in various fields, especially in electrophysiology. Estimation of the EP(evoked potential) signal using the conventional averaging method fails to correctly reconstruct the original signal under EEG(electroencephalogram) noise especial]y when the latency times of the evoked potential are not identical. Therefore, a technique based on the bispectrum averaging was proposed for recovering signal waveform from a set o noisy signals with variable signal dalay. In this paper an improved bispectrum estimation technique of the RP signal is proposed using a confidence thresholding of the EP signal in frequency domain in which energy distribution of the EP signal is usually not uniform. The suggested technique is coupled with the conventional bispectrum estimation technique such as least square method and recursive method. Some results with simulated data and real EP signal are shown.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.117-120
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• 1995

The electronencephalogram (EEG) is a complex electrical signal which reflects generalized brain activity. The EEG is utilized in the clinical assesment of many neurological and psychiatric disorders and offers promise for monitoring of patients undergoing operation. This paper describes a technique for quantitative analysis of EEG signals which is based on an eigenspace. Examples of the application approach to simulated and clinical EEG data illustrate the capabilities.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.109-112
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• 1995

This paper is a study on the detection of the significant point in ECG signal. ECG signal consists of two components; one is high frequency component to be detected and the other is low frequency component to be removed. AR model is appropriate for modelling and removing the low frequency component. AR model coefficients are updated by artificial neural network algorithm. We can remove the background noise(low frequency) by passing through the AR filter. The remaining signals which include high frequency noise are sent to the matched filter to pass only the signal which we want to extract. The template used in matched filter is updated adaptively.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.282-285
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• 1995

In this paper, we present a feedback structured adaptive RF filter based on the recursive modified Gram-Schmidt algorithm for short-term prediction of EEG signal. And the performance of this proposed filter is compared with those of linear AR model, RF filter, Volterra filter and RBF neural network as single-step prediction and multi-step prediction. The results show the superiority of this proposed filter in prediction of EEG signals.

• Journal of the Korean Magnetic Resonance Society
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• v.2 no.2
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• pp.152-157
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• 1998

The diversity of RNA functions ranges from storage and propagation of genetic information to enzymatic activity during RNA processing and protein synthesis. This diversity of functions requires an equally diverse arrays of structures, and, very often, the formation of functional RNA-protein complexes. Recognition of specific RNA signals by RNA-binding proteins is central to all aspects of post-transcriptional regulation of gene expression. We will describe how NMR is being used to understand at the atomic level how these important biological processes occur.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.5-10
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• 1990

The object detection capabilities of ultrasonic imaging systems are limited by the ability of the detection process to distinguish the resolved object signals from backscattered speckle noise. It has been shown that the phase component of the Fourier transform of the speckle noise is random. Based on this property. we propose a new algorithm for distinguishing between speckle and specular targets. The proposed algorithm is implemented by taking the Fourier transform of the received signal, low-pass filtering the phase, and taking the inverse Fourier transform of the filtered phase to enhance specular reflectors and reduce speckle in the image. Simulations and experiments using phantoms confirm the algorithm yielding significant reduction of speckle noise.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.351-360
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• 1989

This paper describes the design of portable arrhythmia monitor and associated algorithm for automated diagnosis based-on microcomputer in the ambulatory ECG recording, analysis, and transmitting to a hospital host computer immediately through the telephone system. The device differs from Molter recorder in that it does not store normal ECG signals but captures and alarms the ECG during suspected abnormal periods and selected temporal epochs to a central hospital site. This porta file arrhythmia monitor makes use of a general purpose computer and software will be changed to meet the custom requirements of individual physicians and patients. At present it is very obvious that each cardiologist has his own method of analyzing ECG recordings and utilizes past experience more than the firm quantitative analysis of data.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.322-325
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• 1997

In this study, we propose a non-invasive method to detect the drowsiness of a driver. The computer vision technology was used to extract an eye, track eyelids and measure the parameters related to the blink. We examined the blink patterns of a driver in drowsy state. For the evaluation of our image processing algorithm, the blink patterns were compared with the measured EOG signals. The result showed that our algorithm might be available in detection of drowsiness.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.263-266
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• 1997

The purpose of this paper is the development of Integrated Bio-signal Management System. (IBMS) using the network. IBMS is the system to manage the medical signals that measured from the each independent medical measurement system module. Each has a LAN Card. We developed the Network Application using Socket Library. Also, we developed the Graphic User Interface software for IBMS using Visual C++ on Windows 95.