• Annual Conference of KIPS
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• 2017.04a
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• pp.669-672
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• 2017

최근 한국인의 주요 사망원인 중 하나로 부정맥이 부각되고 있다. 심방조기수축(PAC:Premature Atrial Contraction)은 심방이 동방결절의 명령이 있기 전에 수축해 버리는 것이다. 심방조기수축은 일시적으로 유발하였다 사라지곤 할 수 있기 때문에 심한 증상이 없다면 생명에 위협을 가하진 않지만 반대의 경우에는 위험할 수 있다. 따라서 비정상적인 심장 박동이 발생하면 이를 검출하여 조기에 부정맥을 진단할 수 있는 방법이 필요하다. 이를 위해 대상의 ECG 신호로부터 QRS패턴에 해당하는 특징들을 추출하였고 특징들을 이용하여 심방조기수축 파형을 분류한다. 오류 역전파 기반으로 특징들을 훈련하며 가중치와 바이어스값을 구한뒤 이를 이용하여 정상파형과 심방조기수축 파형을 분류한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.355-356
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• 2013

심장의 활동을 전기적 변위로 표현되는 심전도 신호는 심장병 진단에 중요한 임상적 파라미터들을 제공한다. 특히 심전도 신호에서 P, QRS Complex,, T 특징점들로 대표되는 파형 변곡점들의 시간상 위치와 크기 및 형태학적 모양은 심장의 이상 리듬을 나타내는 부정맥여부를 검출하는데 핵심적인 역할을 한다. 본 연구에서는 특히 QRS complex 구간에 대한 첨도치의 연산 해석을 통하여 정상적인 심전도 리듬과 심실조기수축 부정맥 리듬을 구분하는 방법을 제시하고 또한 스마트폰을 기반으로 하는 심전도 모니터링 시스템에 적용하고자 하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.15-21
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• 2009

This paper proposes a novel algorithm to assess the abnormal heart beats such as PVC (Premature Ventricular Contraction) and its subsequent RUNs. Our Arrhythmic detection scheme is based on only the R-R Interval features extracted from ECG waveforms and MIT-BIH arrhythmia database is evaluated to validate the efficiency of our algorithm in terms of sensitivity, specificity, FPR(%) and FNR(%).

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.472-473
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• 2008

심실조기수축(PVC: Premature Ventricular Contraction)은 성인에게서 가장 흔하게 발생되는 심장 부정맥 증상 중의 하나이다. 심실조기수축 부정맥이 자주 발현되는 사람의 경우 관상 동맥질환, 고혈압 등의 심혈관계 질환이 진행되고 있을 가능성이 많고, 심실빈맥이나 심실세동으로 전이되는 경우 심정지 등을 유발하여 사망에 이르기 때문에 지속적으로 관찰이 필요한 증상이다. 따라서 본 연구에서는 R-R 간격 정보를 이용하여 심실조기수축 부정맥 증상을 실시간으로 검출할 수 있는 신호처리 알고리즘을 구현하고자 하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.21-26
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• 2008

In this paper, we extracted descending slope tracing waves (DSTW) and form factors (FF), and compared the detecting results of premature ventricular contraction (PVC) which were derived from DSTW and FF in order to find an efficient method. The 2nd. derivatives and DSTW were employed to extract correct R-waves from ECG. To evaluate extracting methods, ECGs including PVCs from MIT/BIH database were used.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.393-395
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• 2009

심장의 활성 근육의 움직임에 의하여 발생되는 전기적 변화량을 나타내는 심전도는 부정맥 또는 허혈성 심장질환을 진단하는데 널리 활용되고 있다. 특히 심실빈맥(Ventricular Tachycardia) 또는 심실세동(Ventricular Fibrillation)과 같이 치명적인 심장리듬이 발생하기 이전에, 심실조기수축(Ventricular Premature Contraction)을 검출하여 생명을 위협할 수 있는 부정맥을 조기에 진단할 수 있는 연구들이 일부 진행되고 있다. 이에 따라서 본 연구에서는 심전도 신호의 R-R 간격 정보와 R-peak 정보의 진위성을 판단하여 PVC 부정맥 패턴뿐만 아니라 PVC 파형이 연속적으로 진행되는 PVC-RUNs을 효율적으로 검출할 수 있는 부정맥 진단 알고리즘을 제안하고자 하였다.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.705-711
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• 2013

QRS detection of ECG is the most popular and easy way to detect cardiac-disease. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, the design of algorithm that exactly detects QRS wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, efficient QRS detection and PVC classification based on profiling method is presented in this paper. For this purpose, we detected QRS through the preprocessing method using morphological filter, adaptive threshold, and window. Also, we applied profiling method to classify each patient's normal cardiac behavior through hash function. The performance of R wave detection, normal beat and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 0.65% in normal beat classification error and 93.29% in PVC classification.

• Clinical and Experimental Pediatrics
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• v.46 no.5
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• pp.490-494
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• 2003

Purpose : We'd like to determine the incidence of congenital heart disease and arrhythmia in elementary school children in Busan, and to provide adequate prevention and treatment. Methods : A total of 23,802(male 12,909, female 10,893) 1st grade elementary school children living in Busan were studied. All children were 7-8 years old. We obtained their medical history by questionnaire and checked elecrocardiography(ECG). Subsequent screening tests including a 2nd ECG, chest X-ray, phonocardiogram and CBC for the students who had abnormal findings at the first screening test. The third screening test was done for students who had cardiac murmurs or abnormal ECG findings in the second screening test by echocardiogram, treadmill test and 24-hour Holter monitoring. Results : Among 23,802 children participants, 605(2.54%) had abnormal ECG findings at the first screening test. Q wave abnormality(0.58%) was observed most frequently, and complete right bundle branch block(RBBB)(0.26%), sinus tachycardia(0.24%), right axis deviation(0.22%) and ventricular premature contraction(VPC)(0.21%) followed in order. Four hundred and twenty four children participated in the second ECG screening test. Two hundred and two children(47.6%) had an abnormality such as sinus tachycardia(18.8%), VPC(17.8%), or complete RBBB(17.3%). After completing the third examination including echocardiogram, we couldn't find the students with ventricular tachycardia (VT) or SVT at the third arrhythmia screening test. Conclusion : A high incidence of arrhythmia was found in the 1st grade elementary school children in Busan despite their healthy appearances, although fatal heart diseases were not noted. Early diagnosis, adequate preventative measures and treatment will prevent and decrease the frequency of emergent situations like syncope and sudden death.

• The Korean Journal of Nuclear Medicine
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• v.28 no.1
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• pp.44-51
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• 1994

It is known that the normal His-Purkinje system provides for nearly synchronous activation of right (RV) and left (LV) ventricles. When His-Purkinje conduction is abnormal, the resulting sequence of ventricular contraction must be correspondingly abnormal. These abnormal mechanical consequences were difficult to demonstrate because of the complexity and the rapidity of it's events. To determine the relationship of the phase changes and the abnormalities of ventricular conduction, we performed phase image analysis of $^{99m}Tc$-RBC gated blood pool scintigrams in patients with intraventricular conduction disturbances (24 complete left bundle branch block (C-LBBB), 15 complete right bundle branch block (C-RBBB), 13 Wolff-Parkinson-White syndrome (WPW), 10 controls). The results were as follows; 1) The ejection fraction (EF), peak ejection rate (PER), and peak filling rate (PFR) of LV in gated blood pool scintigraphy (GBPS) were significantly lower in patients with C-LBBB than in controls ($44.4{\pm}13.9%$ vs $69.9{\pm}4.2%,\;2.48{\pm}0.98$ vs $3.51{\pm}0.62,\;1.76{\pm}0.71$ vs $3.38{\pm}0.92$, respectively, p<0.05). 2) In the phase angle analysis of LV, Standard deviation (SD), width of half maximum of phase angle (FWHM), and range of phase angle were significantly increased in patients with C-LBBB than in controls ($20.6{\pm}18.1$ vs $8.6{\pm}1.8,\;22.5{\pm}9.2$ vs $16.0{\pm}3.9,\;95.7{\pm}31.7$ vs $51.3{\pm}5.4$, respectively, p<0.05). 3) There was no significant difference in EF, PER, PFR between patients with the Wolff-parkinson-White syndrome and controls. 4) Standard deviation and range of phase angle were significantly higher in patients with WPW syndrome than in controls ($10.6{\pm}2.6$ vs $8.6{\pm}1.8$, p<0.05, $69.8{\pm}11.7$ vs $51.3{\pm}5.4$, p<0.001, respectively), however, there was no difference between the two groups in full width of half maximum. 5) Phase image analysis revealed relatively uniform phase across the both ventricles in patients with normal conduction, but markedly delayed phase in the left ventricle of patients with LBBB. 6) In 13 cases of WPW syndrome, the site of preexcitation could be localized in 10 cases (77%) by phase image analysis. Therefore, it can be concluded that phase image analysis can provide an accurate noninvasive method to detect the mechanical consequences of a wide variety of abnormal electrical activation in ventricles.