• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.507-514
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• 1996

The automated ECG diagnostic systems in hospital have a low P-wave detection capacity in case of some diseases like conduction block. The purpose of this study is to improve the P-wave detection ca- pacity using wavelet transform. The first procedure is to remove baseline drift by subtracting the median filtered signal from the original signal. The second procedure is to cancel ECG's QRS-T complex from median filtered signal to get P-wave candidate. Before we subtracted the templete from QRS-T complex, we estimated the best matching between templete and QRS-T complex to minimize the error. Then, wavelet transform was applied to confirm P-wave. In particular, haiti wavelet was used to magnify P-wave that consisted of low frequency components and to reject high frequency noise of QRS-T complex cancelled signal. Finally, p-wave was discriminated and confirmed by threshold value. By using this method, We can got the around 95.1% P-wave detection. It was compared with contextual information.

• Journal of Oriental Neuropsychiatry
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• v.23 no.2
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• pp.33-48
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• 2012

Objectives : This study aimed to assess the effects of incense smokes of 'Cheung-Woon' on the concentration and EEG in healthy individuals. Methods : A total of 48 healthy volunteers participated in this study. The volunteers were examined with K-MAS, CBT(Corsi block tapping task), and EEG before and after smelling the incense smokes of 'Cheung-Woon'. K-MAS measured the recalled words, and CBT measured the recalled positions and orders of the color boxes. EEG measured the relative power of ${\theta}$ wave, ${\alpha}$ wave, SMR wave, mid-${\beta}$ wave, high-${\beta}$ wave, ${\gamma}$ wave and T(concentraion index T = (SMR wave + mid-${\beta}$ wave) / ${\theta}$ wave). 'Cheung-Woon' consists of 7 herbal powder, known as a useful effect on the concentration and memory. Results : After smelling 'Cheung-Woon', K-MAS were increased significantly(p<0.05). In relative power of ${\theta}$ wave, F4, T3, and P4 were decreased significantly(p<0.05) and P3 was also decreased significantly(p<0.01). In the relative power of ${\alpha}$ wave, SMR wave, and mid-${\beta}$ wave, the values were not significant. In the relative power of high-${\beta}$ wave, Fp1, and P4 were increased significantly(p<0.05). In relative power of ${\gamma}$ wave, T3 were increased significantly (p<0.05). In T value, F4, T3, T4, and P4 were increased significantly(p<0.05) and P3 were also increased significantly(p<0.01). Conclusions : This results show that smelling incense smokes of 'Cheung-Woon' is an effective way of increasing concentration and memory.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.35-37
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• 2005

In this study, a P- and T-wave detection algorithm in ECG, obtained by minimized filtering to prevent waveform distortion, has been realized. The algorithm uses two slope tracing waves, the descending slope tracing wave and the ascending slope tracing wave, developed for efficient determination of slope inverting points and sudden slope changing points. The algorithm generates the slope tracing waves which trace the original ECG wave, and subtracts one tracing wave from the other to detect P- and T-waves. The algorithm has been applied to MIT/BIH database in order to verify its efficacy and validity in practical applications.

• Journal of information and communication convergence engineering
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• v.19 no.2
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• pp.93-101
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• 2021

Cardiac arrhythmias are common heart diseases and generally cause sudden cardiac death. Electrocardiogram (ECG) is an effective tool that can reveal the electrical activity of the heart and diagnose cardiac arrhythmias. We propose detection of P waves based on QRST cancellation zero-one substitution. After preprocessing, the QRST segment is determined by detecting the Q wave start point and T wave end point separately. The Q wave start point is detected by digital analyses of the QRS complex width, and the T wave end point is detected by computation of an indicator related to the area covered by the T wave curve. Then, we determine whether the sampled value of the signal is in the interval of the QRST segment and substitute zero or one for the value to cancel the QRST segment. Finally, the maximum amplitude is selected as the peak of the P wave in each RR interval of the residual signal. The average detection rate for the QT database was 97.67%.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.377-380
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• 1996

The purpose of this paper is to improve the P-wave detection capacity using wavelet transform. The first procedure is to remove baseline drift using the median filter. The second procedure is to cancel ECG's QRS-T complex with ECG's QRS-T complex templete to get P-wave candidate. Before we cancelled out the QRS-T complex, we estimated the best matching between templete and QRS-T complex to minimize the error. Then, Harr wavelet was used to eleminate the high frequency noise of ECG wave form cancelled the QRS-T complex. Finally, P-wave was discriminated and confirmed by threshold value. By using this method, We can got the around 95.1% P-wave detection.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1034-1042
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• 2016

The detection of P-waves and T-wave in the electrocardiogram signal analysis is an important issue. But the accuracy of the boundary detection algorithm is an insufficient level in the change of slow transition in the signal compared to the QRS complex. This study proposes an algorithm to detect P-wave and T-wave sequentially after determining local baseline using QRS complex. First, we detected the peak points based on local baseline and determined the onset and offset through the calculation of the area of the section. After modifying the baseline using detected waveform, we detected the other waveform in the same way and separated the P-wave and the T-wave based on the location. We used the PhysioNet QT database to evaluate the performances of the algorithm, and calculate the mean and the standard deviations. The experiment results show that standard deviations are under the tolerances accepted by expert physicians, and outperform the results obtained by the other algorithms.

• Journal of the Korean Mathematical Society
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• v.58 no.3
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• pp.633-642
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• 2021

In this paper we consider the following strongly damped wave equation with variable-exponent nonlinearity u_tt(x, t) - ∆u(x, t) - ∆u_t(x, t) = |u(x, t)|^p(x)-2u(x, t), where the exponent p(·) of nonlinearity is a given measurable function. We establish finite time blow-up results for the solutions with non-positive initial energy and for certain solutions with positive initial energy. We extend the previous results for strongly damped wave equations with constant exponent nonlinearity to the equations with variable-exponent nonlinearity.

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

• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.235-240
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• 1996

The automated ECG diagnostic systems that are odd in hospitals have low performance of P-wave detection when faced with some diseases such as conduction block. So, the purpose of this study was the improvement of detection performance in conduction block which is low in P-wave detection. The first procedure was removal of baseline drift by subtracting the median filtered signal of 0.4 second length from the original signal. Then the algorithm detected R peak and T end point and cancelled the QRS-T complex to get'p prototypes'. Next step was magnification of P prototypes with dispersion and detection of'p candidates'in the magnified signal, and then extraction of contextual information concerned with P-waves. For the last procedure, the CIR was applied to P candidates to confirm P-waves. The rule base consisted of three rules that discriminate and confirm P-waves. This algorithm was evaluated using 500 patient's raw data P-wave detection perFormance was in- creased 6.8% compared with the QRS-T complex cancellation method without application of the rule base.

• Progress in Superconductivity
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• v.9 no.1
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• pp.29-34
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• 2007

The baseline selection is the first and important step to analyze magnetocardiography (MCG) parameters. There are no difficulties to select the baseline between P- and Q-wave peak (P-Q interval) of MCG wave recorded from healthy subjects because the P-Q intervals of the healthy subjects do not much vary. However, patients with ischemic heart disease often show an unstable P-Q interval which does not seem to be appropriate for the baseline. In this case, T-P interval is alternatively recommended for the baseline. However, there has been no study on the difference made by the baseline selection. In this study, we studied the effect of the different baseline selection. MCG data were analyzed from twenty healthy subjects and twenty one patients whose baselines were alternatively selected in the T-P interval for their inappropriate P-Q interval. Paired T-test was used to compare two set of data. Fifteen parameters derived from the R-wave peak, the T-wave peak, and the period, $T_{max/3}{\sim}T_{max}$ were compared for the different baseline selection. As a result, most parameters did not show significant differences (p>0.05) except few parameters. Therefore, there will be no significant differences if anyone of two intervals were selected for the MCG baseline. However, for the consistent analysis, P-Q interval is strongly recommended for the baseline correction.