• 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 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 Biomedical Engineering Research
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• v.24 no.2
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• pp.107-112
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• 2003

This paper describes a new simple QRS detection algorithm using index function based on resonance theory. The ECG signal can be modeled with several sinusoidal pulses and its first difference has some relations with the amplitude and frequency of sinusoidal pulse. Based on above fact, an index function, similar to the square of the imaginary part of a simple R-L-C circuit, was designed. A QRS complex is detected by applying the adaptive method to the response of index function. The algorithm showed a performance comparable to or higher than the other algorithms. Because it does not require any complicated preprocessing or postprocessing, it can be implemented in real time.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.4-55
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• 2001

Analyzing the ECG signal, we can find heart disease. Myocardial ischemia is a disorder of cardiac function caused by insufficient blood flow to the muscle tissue of the heart. Myocardial ischemia is inscribed on ST-segment of the ECG during and after patient takes exercise or is under stress, but after long time past, the ECG pattern is return to steady state. Therefore, it is necessary to monitor and analyze the ECG signal continuously for patient or aged people. Our primary purpose is the detection of temporary change of the ST-segment of ECG automatically. 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 more easily ...

• 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.283-287
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• 1996

This paper described the estimator and eliminator far three kinds of artifacts in electrocardiogram. The most efficient estimation of baseline drift could be obtain in the cubic spline interpolation method with the PQ and TP segment which are considered to be isoelectric, from the experimental results obtained from the applied 4 types of algorithms. The time loss and distortion could be avoided with the aid of detection criteria by checking if baseline drifts exist or not. The AIEF proposed in this paper was verified as having the best removal performance with less distortion in the QRS complex through the comparison of 5 proposed algorithms. furthermore, the AIEF are most suitable far the ECG analyzer which was only needed relatively short time data due to the fast conversion into the stable state. The proposed parabolic filter with 11 points width was identified as having the best performance for the elimination of muscle artifacts. Also we could obtain 99.7% detection accuracy of spike component and minimize the error identifying QRS complex as spike.

• 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.