• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4443-4449
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• 2011

ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1531-1539
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• 2014

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons's physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification.

• The Korean Journal of Physiology
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• v.19 no.1
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• pp.61-72
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• 1985

In order to compare the cardiac function of various groups of athletes, the resting electrocardiographic time intervals, amplitudes and vectors were analyzed in high school athletes of throwing(n=7), jumping(n=11), short track(n=8), long track(n=14), boxing(n=7), volleyball(n=8) and baseball(n=9), and nonathletic control students(n= 19). All athletic groups showed a significantly longer R-R interval(0.96-1.09 sec) than the controls (0.78 sec). Therefore, the heart rate was significantly slower in atheletes than in the control, but was not different among the different athletic groups. R-R interval is the sum of intervals of P-R, 0-T and T-P: P-R and Q-T intervals showed no difference among the control and athletic groups, but T-P interval in the jump, short track, long track and boxing groups was significantly higher than the control. R-B interval showed a significant correlation with T-P or Q-T intervals but no correlation with P-R or QRS complex. Comparing the amplitude of electrocardiographic waves, the athletic groups showed a lower trend in P wave than the controls. T wave in lead $V_5\;(Tv_5)$ was similar in the athletic and control groups. The long track group showed a significantly higher waves of $Rv_5$, $Sv_1$, and the sum of $Rv_5$ and $Sv_1$ than not only the controls but also the other athletic group. The angles of P, QRS, and T vector in the frontal and horizontal planes were not different among the control and all the athletic groups. Each athletic group stowed a lower trend in amplitude of P vector in the frontal plane, but in horizontal plane, throwing, jump, short track and baseball groups showed a significantly lower than the controls. The amplitude of QRS and T vector was similar in the athletic and control groups, but only the baseball group showed a significantly higher QRS vector in the frontal plane. In taken together, all the athletic groups showed a slower heart rate than the controls, mainly because of elongated T-P interval. Comparing the electrocardiographic waves and vector, the athletic groups showed lower amplitudes of P wave and P vector than the controls. Values of $Rv_5$ and $Sv_1$ strongly suggest that only the long distance runners among the various athletic groups developed a left ventricular hypertrophy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2391-2398
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Most of the algorithms detecting PVC reported in literature is not always feasible due to the presence of noise and P wave making the detection difficult, and the process being time consuming and ineffective for real time analysis. To solve this problem, a new approach for the detection of PVC is presented based rhythm analysis and beat matching in this paper. For this purpose, the ECG signals are first processed by the usual preprocessing method and R wave was detected. The algorithm that decides beat type using the rhythm analysis of RR interval and beat matching of QRS width is developed. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate sensitivity of 99.74%, positive predictivity of 99.81% and sensitivity of 93.91%, positive predictivity of 96.48% accuracy respectively for R wave and PVC detection.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.978-980
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• 2014

본 연구에서는 스트레스 상태 측정을 위한 심전도 신호 QRS 검출 알고리즘을 제안한다. 심전도 신호의 QRS 검출 과정은 4단계로 wavelet, moving average, squaring, threshold method로 구성된다. wavelet은 기저선 변동과 노이즈를 제거하고 moving average는 전체 신호를 부드럽게 하고 잔여 노이즈를 제거하며 squaring은 신호를 강조하는 역할을 한다. 마지막으로 threshold 기법을 이용해 검출간격을 설정하여 QRS를 검출하였다. 그 결과 Sensitivity는 99.54%, Positive Predictivity는 99.69%, Detection Error는 0.76%를 보였다. 또한, 피험자를 대상으로 게임을 이용해 스트레스 상태 변화에 대한 실험을 하였고, HRV 시간-주파수 파라미터를 분석함으로써 스트레스 상태 변화를 관찰할 수 있었다.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.269-278
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• 1998

This paper describes a development of efficient stress ECG signal analysis algorithm. The algorithm consists of wavelet adaptive filter(WAF), QRS detector and ST segment detector. The WAF consists of a wavelet transform and an adaptive filter. The wavelet transform decomposed the ECG signal into seven levels using wavelet function for each high frequency bank and low frequency bank. The adaptive filter used the signal of the seventh lowest frequency band among the wavelet transformed signals as primary input. For detection of QRS complex, we made summed signals that are composed of high frequency bands including frequency component of QRS complex and applied the adaptive threshold method changing the amplitude of threshold according to RR interval. For evaluation of the performance of the WAF, we used two baseline wandering elimination filters including a standard filter and a general adaptive filter. WAF showed a better performance than compared filters in the noise elimination characteristics and signal distortion. For evaluation of WAF showed a better performance than compared filters in the noise elimination characteristics and signal distortion. For evaluation of results of QRS complex detection, we compared our algorithm with existing algorithms using MIT/BIH database. Our algorithm using summed signals showed the accuracy of 99.67% and the higher performance of QRS detection than existing algorithms. Also, we used European ST-T database and patient data to evaluate measurement of the ST segment and could measure the ST segment adaptively according to change of heart rate.

• Clinical and Experimental Pediatrics
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• v.52 no.12
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• pp.1364-1369
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• 2009

Purpose : To analyze abnormal ventricular activation in childhood congenital heart disease induced by postoperative changes in ventricular volume and pressure and ventricular scar formation using signal-averaged electrocardiography (SAECG). Methods : Fifty-two patients who had undergone open heart surgery (OHS) were enrolled. Patients were divided into the following 3 groups: right ventricular volume overload (atrial septal defect, group1), left ventricular volume overload (ventricular septal defect, group2), and right ventricular pressure overload (tetralogy of Fallot, group 3). The patients were monitored by standard 12-lead ECG and SAECG before and 2 months after the operation. QRS duration, QT and QTc intervals, filtered QRS (f-QRS), high frequency low amplitude potential (HFLA), and root mean square (RMS) voltage in the terminal 40 ms of SAECG were determined. Results : In the preoperative period, group1 showed significant increase in QRS (P=0.011) compared to those of the other 2 groups. In the postoperative period, group3 showed significant increase in the QTc interval (P=0.004) compared to those in the other 2 groups. SAECG parameters showed no significant differences among the groups in the pre- or postoperative period. Of the 52 patients, 12 (23%) in the preoperative period and 21 (40%) in the postoperative period had at least 1 SAECG abnormality. The prevalence of SAECG abnormalities was significantly higher in the postoperative group 2 and group 3 (preoperative: 20% versus postoperative: 28%, P<0.001, preoperative: 14% versus postoperative: 64%, P<0.001, respectively). Conclusion : Abnormal SAECG patterns may be attributed to postoperative scars, OHS itself, and/or ventricular overload.

• Korean Journal of Biological Psychiatry
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• v.1 no.1
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• pp.124-128
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• 1994

We report a case of antipsychotics induced torsade de pointes in a 42-year-old female schizophrenic patient. The patient had taken perphenazine 20 mg/day, chlorpromazine 100 mg/day, and trifluoperazine 15 mg/day irregularly for about 8 years. She experienced syncope and a few difficulties in breathing. On EKG(electrocardiography), QT interval was delayed and polymorphic QRS complexes and ventricular tachycardia were observed. Following a switch of the antipsychotics to haloperidol, known to have fewest effects on the cardiac rhythms among antipsychotics, the arhythymias disappeared. However after discharge, as dose of haloperidol was increased, the symptoms such as chest discomforts and syncopes reappeared. We concluded that the torsade de pointes was developed by antipsychotics. The most common cause of sudden death in patients receiving antipsychotic treatment appears to be ventricular tochycardia. Therefore, clinician should be well aware of the possible side effects of antipsychotics and be cautious in prescribing such drugs to their patients.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.63-71
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• 2011

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.