• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.277-280
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• 1995

This paper describes a real time QRS detection algorithm. The proposed algorithm detects QRS complex using characteristics of the 2-dimensional phase portrait which is reconstructed from 1-demensional scalar time series. We observe the phase portrait of ECG signal has special trejectory when QRS complex occurs and apply it to detect QRS complexes. In order to evaluate the performance of the proposed algorithm, we use MIT/BIH arrhythmia database. As a result, the proposed algorithm correctly detects 99.3% of the QRS complexes.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.483-486
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• 2000

심전도에서 QRS complex와 R-wave의 검출은 부정맥 진단, 심전도의 특성점 검출 기준, heart rate variability(HRV) 측정에 있어서 중요하나, 시시각각 변화하는 생리적 변화와 여러 가지 노이즈로 인해 검출이 쉽지 않다 제안된 알고리듬에서는 wavelet filter banks를 이용하여 대칭적 enhanced 신호와 noise 와 같은 very high frequency 성분이 제거된 ECG에 근사화 된 approximated 신호를 얻는다. Enhanced 신호로부터 QRS complex의 위치를 검출하고, 검출된 위치의 주변에서 대칭적 wavelet의 특성이 반영된 dominant한 peak의 위치정보, 즉 R wave의 후보점을 얻는다. 이 위치 정보를 이용하여 enhanced 신호에서 각 peak에서의 크기, approxi-mated 신호에서 각 peak 주변에서의 기울기 변화, 기울기 부호 등을 고려하여 R-wave의 위치를 원래의 ECG 신호에서 얻는다. MIT/BIH database에 적용한 결과 99.6%의 QRS complex검출률과 92.9%의 R-wave 검출률을 보였다.

• 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 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 Biomedical Engineering Research
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• v.5 no.2
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• pp.173-182
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• 1984

This paper represents a algorithm which improves the some drawbacks in the past methods for detecting QRS Complex waves. This proposed algorithm is very useful to detect correctly QRS Complex not only in a normal ECG, but in the abnormal ECG such as contaminating the noise with high amplitude, the existence of sharp T wave, and abrupt stepwise fluctuation of the base line.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.3
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• pp.40-47
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• 2010

In this study, we investigated the performance of real-time QRS complex detection algorithm in physio-module for echocardiography. The performance of QRS detection module in echocardiography was evaluated according to international standard, EC-13 and we compared with commercialized physio-module with QRS complex detection. In this study, we can get performance of QRS complex detection, pacer pulse detection, Tall t-wave rejection and arrhythmia detection within EC-13's criteria and we can get improved QRS trigger delay time and baseline wondering rejection times in compared with commercialized physio-module.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.129-135
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• 2004

In the home health management system, we often face the situation to handle biological signals that are frequently measured from normal subjects. In such a case, it is necessary to decide whether the signal at a certain moment is normal or abnormal. Since ECC is one of the most frequently measured biological signals, we describe algorithms that detect QRS-complex and decide whether it is normal or abnormal. The developed QRS detection algorithm is a simplified version of the conventional algorithm providing enough performance for the proposed application. The developed classification algorithm that detects abnormal from mostly normal beats is based on QRS width, R-R interval and QRS shape parameter using Karhunen-Loeve transformation. The simplified QRS detector correctly detected about 99% of all beats in the MTT/BIH ECG database. The classification algorithm correctly classified about 96% of beats as normal or abnormal. The QRS detection and classification algorithm described in this paper could be used in home health management system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.731-739
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• 2015

In arrhythmia ECG signal, abnormal beat that has various abnormal shape depending on the generation site and conduction disorders is included and it is very important to diagnose heart disease such as arrhythmia. In this paper, we propose a PVC abnormal beat detection algorithm associated with ventricular disease. The PVC abnormal beat is characterized by distortion of the QRS complex occurs among the components of the ECG signal. Therefore it is possible to detect PVC abnormal beat according to the degree of distortion of the QRS complex. First, quantify the distortion of the QRS complex by using the potential of the R-peak, kurtosis and period. By using the mean and standard deviation, PVC abnormal beat is detected depending on the degree of distortion from the normal beat. The proposed algorithm can detect the average over 98% of the AAMI-V class type abnormal beat associated with ventricular disease in MIT-BIH arrhythmia database.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.294-294
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• 2000

In this paper, we would like to discuss the signal processing and the algorithm for ECG analysis. The ECG gives us information about the condition of the heart muscle, because myocardial abnormality or infarction is inscribed on the ECG during myocardial depolarization and repolarization. Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. The wavelet transform decomposes the ECG signal into high and low frequency component using wavelet function. Recomposing high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the curve-fitting partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with some kinds of heart disease ECG pattern, we can detect and classify the kind of heart disease.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.13-16
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• 2019

The analysis of electrocardiogram (ECG) signals facilitates the detection of various abnormal conditions of the human heart. The QRS complex is the most critical part of the ECG waveform. Further, different diseases can be identified based on the QRS complex. In this paper, a new algorithm based on the well-known Pan-Tompkins algorithm has been proposed. In the proposed scheme, the QRS complex is initially extracted by removing the background noise. Subsequently, the R-R interval and heart rate are calculated to detect whether the ECG is normal or has some abnormalities such as tachycardia and bradycardia. The accuracy of the proposed algorithm is found to be almost the same as the Pan-Tompkins algorithm and increases the R peak detection processing speed. For this work, samples are used from the MIT-BIH Arrhythmia Database, and the simulation is carried out using MATLAB 2016a.