• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.39-46
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• 2012

Electrical impedance tomography(EIT) can produce functional images with conductivity distributions associated with physiological events such as cardiac and respiratory cycles. EIT has been proposed as a clinical imaging tool for the detection of stroke and breast cancer, pulmonary function monitoring, cardiac imaging and other clinical applications. However EIT still suffers from technical challenges such as the electrode interface, hardware limitations, lack of animal or human trials, and interpretation of conductivity variations in reconstructed images. We improved the KHU Mark2 EIT system by introducing an EIT electrode interface consisting of nano-web fabric electrodes and by adding a synchronized biosignal measurement system for gated conductivity imaging. ECG and respiration signals are collected to analyze the relationship between the changes in conductivity images and cardiac activity or respiration. The biosignal measurement system provides a trigger to the EIT system to commence imaging and the EIT system produces an output trigger. This EIT acquisition time trigger signal will also allow us to operate the EIT system synchronously with other clinical devices. This type of biosignal gated conductivity imaging enables capture of fast cardiac events and may also improve images and the signal-to-noise ratio (SNR) by using signal averaging methods at the same point in cardiac or respiration cycles. As an example we monitored the beat by beat cardiac-related change of conductivity in the EIT images obtained at a common state over multiple respiration cycles. We showed that the gated conductivity imaging method reveals cardiac perfusion changes in the heart region of the EIT images on a canine animal model. These changes appear to have the expected timing relationship to the ECG and ventilator settings that were used to control respiration. As EIT is radiation free and displays high timing resolution its ability to reveal perfusion changes may be of use in intensive care units for continuous monitoring of cardiopulmonary function.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.495-503
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• 2000

스트레스 심전계에서 발생되는 근잡음을 제거하기 위하여 wavelet interpolation filter(WIF)를 설계하였다. WIF는 크게 웨이브렛 변환부와 보간법 적용부로 구성되어 있다. 웨이브렛 변환부는 Haar 웨이브렛을 이용하였으며 심전도 저주파 영역과 고주파 영역으로 분할하는 과정이다. 보간법 적용부에서는 분할되어진 신호 중 A3을 선택하여 신호의 재생 성능을 향상시키기 위하여 보간법을 적용하였다. WIF의 성능을 평가하기 위해서 신호대 잡음비, 재생신호 자승오차 및 표준편차의 파라미터를 이용하였다. 본 실험에서는 MIT/BIH 부정맥 데이터베이스, European ST-T 데이터베이스 및 삼각파형을 이용하여 성능 파라미터를 측정하였다. 결과적으로 WIF는 성능 파라미터에서 기존에 많이 사용되고 있는 평균값 필터, 중간값 필터 및 hard thresholding 방법에 비해 우수함을 알 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.427-436
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• 2016

In general, QRS duration represent a distance of Q start and S end point. However, since criteria of QRS duration are vague and Q, S point is not detected accurately, arrhythmia classification performance can be reduced. In this paper, we propose extraction of Q, S start and end point RS feature based on phase transition tracking method after we detected R wave that is large peak of electrocardiogram(ECG) signal. For this purpose, we detected R wave, from noise-free ECG signal through the preprocessing method. Also, we classified QRS pattern through differentiation value of ECG signal and extracted Q, S start and end point by tracking direction and count of phase based on R wave. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 99.60%. PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction(PVC). The achieved scores indicate the average detection rate of 94.12% in PVC.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.41-49
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• 2003

To inspect how the different sensitivities in BAS(or BIS) modulate on the HRV pattern stimulated by positive or negative affective sound, we measured the electrocardiogram(ECG) of 25 students(male : 14), consisted of 4 groups depending on the BAS(or BIS) sensitivity, during listening meditation music or being exposed to noise. The power spectral density(PSD) of HRV was derived from the ECG, and the power of HRV was calculated for 3 major frequency ranges(low frequency[LF], medium frequency[MF], and high frequency[HF]). We found that the index of MF/(LF+HF), during listening music, was higher significantly in the individuals with a low BIS but high BAS than in the individuals with a low sensitivity in both BIS and BAS. Especially in the former group, there was a tendency that the index was higher during listening music than during being exposed to noise. For individuals with a high BIS, regardless of the BAS sensitivity, the difference of this index values was not significant. From these results we suggest that individuals with a low BIS but high BAS are more sensitive to positive affective stimuli than other groups, and the index of MF/(LF+HF) is applicable to evaluate positive and negative affects.

• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.449-458
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• 1996

Most of the conventional electrocardiowaphs foil to detect signals other than P-QRS-T due to the limited SNR and bandwidth. High-resolution electrocardiography(HRECG) provides better SNR and wider bandwidth for the detection of micro-potentials with higher frequency components such as vontricular late potentials(LP). We have developed a HRECG using uncorrected XYZ lead for the detection of LPs. The overall gain of the amplifier is 4000 and the bandwidth is 0.5-300Hz without using 60Hz notch filter. Three 16-bit A/D converters sample X, Y, and Z signals simultaneously with a sampling frequency of 2000Hz. Sampled data are transmitted to a PC via a DMA-controlled, optically-coupled serial communication channel. In order to further reduce the noise, we implemented a signal averaging algorithm that averaged many instances of aligned beats. The beat alignment was carried out through the use of a template matching technique that finds a location maximizing cross-correlation with a given beat tem- plate. Beat alignment error was reduced to $\pm$0.25ms. FIR high-pass filter with cut-off frequency of 40Hz was applied to remove the low frequency components of the averaged X, Y, and Z signals. QRS onset and end point were determined from the vector magnitude of the sigrlaIL and some parameters needed to detect the existence of LP were estimated. The entire system was designed for the easy application of the future research topics including the optimal lead system, filter design, new parameter extraction, etc. In the developed HRECG, without signal averaging, the noise level was less than 5$\mu$V$_rms RTI$. With signal averaging of at least 100 beats, the noise level was reduced to 0.5$\mu$V$_rms RTI$, which is low enough to detect LPs. The developed HRECG will provide a new advanced functionality to interpretive ECG analyzers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1587-1595
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• 2016

P wave is cardiac parameters that represent the electrical and physiological characteristics, it is very important to diagnose atrial arrhythmia. However, It is very difficult to detect because of the small size compared to R wave and the various morphology. Several methods for detecting P wave has been proposed, such as frequency analysis and non-linear approach. However, in the case of conduction abnormality such as AV block or atrial arrhythmia, detection accuracy is at the lower level. We propose P wave detection algorithm through adaptive threshold and QRS peak variability. For this purpose, we detected Q, R, S wave from noise-free ECG signal through the preprocessing method. And then we classified three pattern of P wave by peak variability and detected adaptive window and threshold. The performance of P wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 92.60%.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.47-54
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• 2019

We examined previous studies of the correlation analysis of heart rate variability as a method to reduce the stress caused by outside noise during driving, and we investigated whether there are electrocardiographic changes when drivers play music, which provides a stable sound source amid the noise. Because the number of cars increases every year, drivers and passengers show an increase in stress caused by outside noise. The stress from outside noise while a person is driving can cause several disorders, such as anxiety, immunosuppression, depression, and heart disease. Subjects in this study operated a vehicle simulator to reduce the stress from outside noise and were given different auditory stimuli, and we studied the drivers' responses to the stimuli. Repeated-measures analysis of variance revealed a significant differences between subjects exposed to different auditory stimuli (ρ < 0.05). Through post hoc analyses, we examined these differences. We found significant differences between factor 1 (stability) and factor 2 (simulation driving), between factor 1 (stability) and factor 3 (driving + police siren), and between factor 1 (stability) and factor 4 (driving + police siren + music). In addition, the factor that produced the highest level of sympathetic nervous system activity was factor 4 (driving + police siren + music), followed by factor 3 (driving + police siren), factor 2 (driving), and factor 1 (stability). In conclusion, even when a police siren was heard during driving, there were no significant differences on electrocardiograms (ECGs). In addition, even when the siren was heard over the music, there was no difference on the ECGs (ρ < 0.01). In future studies, investigators should determine which types of music help stabilize the heart rate during driving.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.277-283
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• 2023

Vital signals provide essential information regarding the health status of individuals, thereby contributing to health management and medical research. Present monitoring methods, such as ECGs (Electrocardiograms) and smartwatches, demand proximity and fixed postures, which limit their applicability. To address this, Non-contact vital signal measurement methods, such as CW (Continuous-Wave) radar, have emerged as a solution. However, unwanted signal components and a stepwise processing approach lead to errors and limitations in heart rate detection. To overcome these issues, this study introduces an integrated neural network approach that combines noise removal, demodulation, and dominant-frequency detection into a unified process. The neural network employed for signal processing in this research adopts a MLP (Multi-Layer Perceptron) architecture, which analyzes the in-phase and quadrature signals collected within a specified time window, using two distinct input layers. The training of the neural network utilizes CW radar signals and reference heart rates obtained from the ECG. In the experimental evaluation, networks trained on different datasets were compared, and their performance was assessed based on loss and frequency accuracy. The proposed methodology exhibits substantial potential for achieving precise vital signals through non-contact measurements, effectively mitigating the limitations of existing methodologies.

• Science of Emotion and Sensibility
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• v.8 no.1
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• pp.47-54
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• 2005

This study is aimed to inspect how the different sensitivities in Behavioral activation system(BAS) and behavioral inhibition system(BIS) modulate on the properties of physiological responses stimulated by positive or negative affective sound. We measured the electroencephalogram(EEG) and electrocardiogram (ECG) of 32 students, consisted of four groups depending on the BAS and BIS sensitivities, during listening to meditation music or noise. The EEG was recorded at Fpl and Fp2 sites and Power spectral density(PSD) of HRV was derived from the ECG, and the power of HRV was calculated for 3 major frequency ranges(low frequency[LF], medium frequency and high frequency[HF]). After listening to music or noise, subjects reported the affect induced by the sound. For EEG, the power in the alpha band at Fp2, especially in the alpha-2 band(9.0-11.0 Hz) increased during the subjects listening to music, while the power at Fpl increased during noise. During listening to meditation music, there is a tendency that the left-sided activation in prefrontal cortex(PFC) is positively correlated with the difference of BAS(Z)-BIS(Z). During listening to noise, there is a tendency that the right-sided activation in PFC is dominant in case any of the sensitivity of BAS or BIS is high. For HRV, we found that the index of MF/(LF+HF), during listening to music, was higher significantly in the individuals with a low BIS but high BAS than in the individuals with a low sensitivity both BIS and BAS individuals. With high BIS, regardless of the BAS sensitivity, the difference of this index values was not significant. From these results we suggest that the physiological responses of different individuals in BAS and BIS react differently under the same emotionally provocative challenge.