• Journal of Sensor Science and Technology
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• v.22 no.5
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• pp.338-345
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• 2013

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation like ventricular fibrillation and ventricular tachycardia in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and the prevention of possible life threatening cardiac diseases. Most methods for detecting arrhythmia require pp interval, or the diversity of P wave morphology, but they are difficult to detect the p wave signal because of various noise types. Thus, it is necessary to use noise-free R wave. So, the new approach for the detection of PVC is presented based on the rhythm analysis and the beat matching in this paper. For this purpose, we removed baseline wandering of low frequency band and made summed signals that are composed of two high frequency bands including the frequency component of QRS complex using the wavelet filter. And then we designed R wave detection algorithm using the adaptive threshold and window through RR interval. Also, we developed algorithm to classify PVC using RR interval. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate average detection rate of 99.76%, sensitivity of 99.30% and specificity of 98.66%; accuracy respectively for R wave and PVC detection.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.96-101
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• 2011

In recent days, the demand for the remote ECG monitoring system has been increasing and the automation of the monitoring system is becoming quite of a concern. Automatic detection of the abnormal ECG beats must be a necessity for the successful commercialization of these real time remote ECG monitoring system. From these viewpoints, in this paper, we proposed an automatic detection algorithm for the abnormal ECG beats using QRS width and RR interval patterns. In the previous research, many efforts have been done to classify the ECG beats into detailed categories. But, these approaches have disadvantages such that they produce lots of misclassification errors and variabilities in the classification performance. Also, they require large amount of training data for the accurate classification and heavy computation during the classification process. But, we think that the detection of abnormality from the ECG beats is more important that the detailed classification for the automatic ECG monitoring system. In this paper, we tried to detect the VEB which is most frequently occurring among the abnormal ECG beats and we could achieve satisfactory detection performance when applied the proposed algorithm to the MIT/BIH database.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1276-1279
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• 1987

Cardiac arrhythmias are associated with electrical Instability and, hence, with abnormal mechanical activity of the heart in many cases, arrhythmias can be treated with drugs or electric shock to control and/or stop them. Hence fast arrhythmia detection is very important. In this paper RR interval, QRS width, and morphology are used for diagnosis and QRS complex is detected by hardware system. hence diagnosing time is shorten. Moreover doctors or nurses who have little knowledge of computer manipulation can get the Information of Patient's ECG by showing characteristics of abnormal waveform and by mapping graphs of RR interval .vs. QRS width and RR interval .vs. morphology on screen.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.167-170
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• 1993

matched filter는 신호와 잡음의 통계적 값을 알고 있을 때 신호대 잡음비를 최대로 하는 filter이다. 그런데, matched filter가 최적화 되려면 잡음이 white noise이어야한다. 그러나 ECG신호에 존재하는 잡음은 여러가지 성분이 공존하는 서로 연관되어있는 잡음이다. 따라서 whitening filter를 사용하여 잡음을 whitening시킨후에 matched filter를 통과 시켜야한다. 본 논문에서는 QRS complex를 검출하기 위한 matched filter에 있어서 LMS방법을 이용한 linear whitening filter와 neural network을 이용한 non-linear whitening filter의 특성을 비교하였다.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.42-47
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• 2001

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. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the 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 polynomial approximation 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 the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.277-284
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• 1991

This study describes the ambulatory ECG monitoring system for the remote autom atic diagnosis. System: tlardware is based on one chip microcomputer(80c31) and its peripherals which consists of A/D, EPROM, RAM, LCD display and two preamplifiers, Power circuits, control logic circuits. A/D converted data were differentiated and low pass filtered. The detection of QRS complex and R point were accomplished by software algorithm based on adaptive threshold computed on low pass fi:leered signal. Rhythm analysis is performed by RR interval and average RR interval. The performance of QRS detection algorithm is evaluated by using MIT/BIH data base. Using this system, the trends of the arrythmia during the long term could be saved and displayed.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.288-292
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• 1996

This paper describes far the detection of heart event that is, QRS complex and P wave which are result from heart activity. The proposed QRS detection method by using the spatial velocity was identified as having the 99.6% detection accuracy as well as fast processing time. Atrial flutter, coupled P wave, and noncoupled P wave as well as atrial fibrillation could be detected correctly by three different algorithms according to their origination farm. About 99.6% correction accuracy coupled P wave could be obtained and we could be found that most detection errors are caused by establishing wrong search interval.

• 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 Korea Multimedia Society
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• v.19 no.5
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• pp.818-825
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• 2016

The existing R-peak detection research suggests improving the distortion of the signal such as baseline variations in ECG signals by using preprocessing techniques such as a bandpass filtering. However, preprocessing can introduce another distortion, as it can generate a false detection in the R-wave detection. In this paper, we propose an R-peak detection algorithm in ECG signal, based on primitive signal in order to detect reliably an R-peak in baseline variation. First, the proposed algorithm decides the primitive signal to represent the QRS complex in ECG signal, and by scaling the time axis and voltage axis, extracts multiple primitive signals. Second, the algorithm detects the candidates of the R-peak using the value of the voltage. Third, the algorithm measures the similarity between multiple primitive signals and the R-peak candidates. Finally, the algorithm detects the R-peak using the mean and the standard deviation of similarity. Throughout the experiment, we confirmed that the algorithm detected reliably a QRS group similar to multiple primitive signals. Specifically, the algorithm can achieve an R-peak detection rate greater than an average rate of 99.9%, based on eight records of MIT-BIH ADB used in this experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.192-200
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• 2015

Several algorithms have been developed to classify arrhythmia which rely on specific ECG(Electrocardiogram) database. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to classify the pattern by analyzing personalized ECG signal and extracting minimal feature. Thus, QRS pattern Analysis of personalized ECG Signal by Q, R, S peak variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and extract eight feature by amplitude and phase variability. Also, we classified nine pattern in realtime through peak and morphology variability. PVC, PAC, Normal, LBBB, RBBB, Paced beat arrhythmia is evaluated by using 43 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 93.72% in QRS pattern detection classification.