• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.4
• /
• pp.825-832
• /
• 2014

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 accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.61-68
• /
• 2010

The objective of this study was to test the estimation possibility of driver's stress degrees with the HRV analysis. For this, first, HRV analysis was applied to the 5 driver's ECG signals which were acquired in 7 different stress situations. From this, the facts that HRV trend was different from that in long-distance driving and 6 parameters - meanRR, sdRR, HF, LF/HF, LFnorm, HFnorm were useful for the stress estimation in stress varying driving situation. Next, we designed 5 personalized linear regression models in which 6 HRV parameters were input and the outcomes were 7 different stress degrees. Finally, we tested each model for 5min-long 16 segments individually. Consequently, the models could not hit the stress degrees exactly in some segments but the correlation coefficients between original stress pattern and estimated stress pattern during entire driving showed reasonably high.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.10 no.2
• /
• pp.132-150
• /
• 2006

Objectives : The objective is that we measure and analyze Pulse-Respiration Ratio and Body Composition Analysis to study the correlation between both. Methods : First, after subjects take a rest over 10 minutes, we measure their electrocardiogram and respiration pattern through which we take average peak interval to calculate an average pulse cycle and a respiration cycle. An average respiration cycle divided by an average Pulse Rate gives Pulse-Respiration Ratio. Next, we draw out 22 Body Composition Analysis indicators by using In-Body 720 model. Last, we analyze and take statistics on them by using SPSS 13.0 program. Results : Negative is the correlation between P/R Ratio and Body Composition Analysis indicator like fatness degree, body fat volume, body fat rate, abdominal fatness, BMI. Conclusions : 1. The higher P/R Ratio the more likely to be thin, the lower P/R Ratio the more likely to be fat. 2. We separately analyze P/R Ratio depending on each breathing frequency and pulse frequency to find out that breathing frequency has great influence and that breathing frequency decides the fatness degree. 3. In study on the correlation between P/R Ratio and Body Composition Analysis, fatness degree, in-body fat volume, in-body fat rate, BMI are the related indicators, which shows the connection with the fatness indicators. 4. In study on the correlation between Han-Yeol [寒熱] grade and Body Composition Analysis indicators, the result is that Han[寒] has no connection and that only Yeol[熱] grade has something to do with it, which means the higher heat symptom subjects have, the more basic metabolism volume and muscular build they have.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.2
• /
• pp.450-460
• /
• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.2
• /
• pp.988-1001
• /
• 2018

Arrhythmia has recently emerged as one of the major causes of death in Koreans. Premature Ventricular Contraction (PVC) is the most common arrhythmia that can be found in clinical practice, and it may be a precursor to dangerous arrhythmias, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect an abnormal heart beat and diagnose arrhythmia early. We extracted the features corresponding to the QRS pattern from the subject's ECG signal and classify the premature ventricular contraction waveform using the features. We modified the weighting and bias values based on the error back-propagation algorithm through learning data. We classify the normal signal and the premature ventricular contraction signal through the modified weights and deflection values. MIT-BIH arrhythmia data sets were used for performance tests. We used RR interval, QS interval, QR amplitude and RS amplitude features. And the hidden layer with two nodes is composed of two layers to form a total three layers (input layer 0, output layer 3).

• The Korean Journal of Emergency Medical Services
• /
• v.24 no.3
• /
• pp.141-145
• /
• 2020

The most common symptom of aortic dissection is chest pain, which is similar to acute coronary artery syndrome, making it difficult to diagnose with clinical pattern, requiring various diagnostic methods. About 10-15% of the aortic dissection patients are accompanied by changes in the ST segment by the dissecting flap of the coronary opening, which can lead to delayed diagnosis of aortic dissection, or can adversely affect the patient by administration to unnecessary drugs such as nitroglycerin, thrombolytic agent, and anticoagulants. It is difficult to distinguish aortic dissection from an acute myocardial infarction only through a 12-Lead electrocardiogram at the pre-hospital. The application of cardiac ultrasonography through medical direction to chest pain patients who show ST segmental changes in pre-hospital phase will contribute to the diagnosis of aortic dissection and the improvement of survival rate, such as anticoagulant administration, to patients with acute myocardial infarction.

• Science of Emotion and Sensibility
• /
• v.6 no.4
• /
• pp.41-49
• /
• 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
• /
• v.16 no.1
• /
• pp.95-100
• /
• 1995

In this study, we have evaluated the effect of amplitude and frequency perturbation of EGG signal for single vowels associated with laryngeal pathology. The normal EGG signal was properly characterized by an autoregressive model which has an optimal order of ninth using the parametric method. This can be analyzed by determining the transfer function. Perturbations in the fundamental pitch and in the peak amplitude of EGG signal measured with a four-electrode system using the modulation/demodulation techniques were investigated for the purpose of developing a decision criteria for the laryngeal function analysis. The abnormal EGG signal has nonperiodic and unstable characteristics. It can be discriminated by the calculation of opening and closing time of glottis using the EGG signal. In case of normal and abnormal subjects, m$\pm$0.5*sd was discriminating line for frequency perturbation and m$\pm$2*sd for normal amplitude perturbations, respectively. Also, The normal and abnormal cases of the subjects can be discriminated effectively using the pattern of attractor derived with Hilbert transform of EGG signal.

• Journal of Korea Game Society
• /
• v.17 no.2
• /
• pp.17-26
• /
• 2017

Excessive use of the internet and smart phones have become a social issue. The level of engagement has both positive and negative effects such as good performance or indulgence phenomenon, respectively. This study was to develop an algorithm to determine the engagement state based on cardiovascular response. The participants were asked to play a pattern matching game and the experimental design was divided into cooperation and competition task to provide the level of engagement. The correlation between heart rate and amplitude was analyzed according to each task. The regression equation and accuracy were verified by polynomial regression analysis. The results showed that heart rate and amplitude were positively correlated when the task was a game, and negatively correlated when there was a reference task. The accuracy of classifying between game and reference task was 89%. The accuracy between tasks was confirmed to be 76.5%. This study is expected to be used to quantitatively evaluate the level of engagement in real time.

• Proceedings of the ESK Conference
• /
• 1996.10a
• /
• pp.47-55
• /
• 1996

This study developed a comprehensive and easily applicable nuclear reactor control system evaluation method using reactor operators behavioral and mental workload database. A proposed control panel design cycle consists of the 5 steps: (1) finding out inconvenient, erroneous, and mentally stressful factors for the proposed design through evaluative experiments, (2) drafting improved design alternatives considering detective factors found out in the step (1), (3) comparative experiements for the design alternatives, (4) selecting a best design alternative, (5) returning to the step (1) and repeating the design cycle. Reactor operators behavioral and mental workload database collected from evaluative experiments in the step (1) and comparative experiments in the step (3) of the design cycle have a key roll in finding out defective factors and yielding the criteria for selection of the proposed reactor control systems. The behavioral database was designed to include the major informations about reactor operators' control behaviors: beginning time of operations, involved displays, classification of observational behaviors, dehaviors, decisions, involved control devices, classification of control behaviors, communications, emotional status, opinions for man-machine interface, and system event log. The database for mental workload scored from various physiological variables-EEG, EOG, ECG, and respir- ation pattern-was developed to indicate the most stressful situation during reactor control operations and to give hints for defective design factors. An experimental test for the evaluation method applied to the Compact Nuclear Simulator (CNS) installed in Korea Atomic Energy Research Institute (KAERI) suggested that some defective design factors of analog indicators should be improved and that automatization of power control to a target level would give relaxation to the subject operators in stressful situation.