• 대한의용생체공학회:의공학회지
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• 제12권1호
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• pp.63-70
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• 1991

Design of Arrhythmia Automatic Diagnostic System Using Decision Table We have developed an arrhythmia automatic diagnostic system using decision table which is based on the criteria of Minnesota code. This system is divided into two Parts. One is wave detection algorithm using significant point extraction method, the other is arrhythmia diag- nostic algorthm. The proposed system allows physicians to diagnose more accurately by pro- viding the objective information about a lot of computer -processed ECG data.

• 센서학회지
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• 제22권5호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.746-749
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• 1988

This paper describes an arrhythmia detection algorithm using PP and PR Interval. In order to detect P-wave accurately, an improved 5-point derivative method is used. In this paper, the RR, PP and PR interval. and the number of P-waves per RR Interval are detected for arrhythmia detection. These parameters can be utilized to diagnose in the varied types of AV block, atrial fibrillation, and PVC.

• International Journal of Advanced Culture Technology
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• 제11권2호
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• pp.310-314
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• 2023

In this paper, we show that the limitations of Holter monitoring and Wearable Electrocardiogarphy Devices and their arrhythmia detection. Sudden death caused by cardiovascular disease, often referred to as the "silent killer" due to its unpredictable nature, is a major health concern. Electrocardiography (ECG) is a basic diagnostic tool for detecting heart disease, but its limitations make it difficult to detect arrhythmia, a significant indicator of an irregular heart state. To address this limitation, a long-term continuous ECG recording device has been developed, Holter ECG device and wearable device. A significant number of studies have focused on the differences between Holter monitoring and wearable devices. The Holter tests were useful for detecting regularly occurring arrhythmias, whereas wearable patches were better at detecting random and infrequent arrhythmias. Wearable patches were effective in detecting episodes of arrhythmia and myocardial ischemia. Despite the concern, wearable devices had less signal loss than Holter monitoring and patients also preferred wearable devices over Holter monitoring due to convenience. These results could mean that the wearable devices can perfectly replace the Holter test.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.29-31
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• 2005

Detection methods for atrial tachycardia and fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.142-145
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• 2006

Detection methods for atrial tachycardia and atrial fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and atrial fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.

• 한국산학기술학회논문지
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• 제12권10호
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• pp.4443-4449
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• 2011

심전도는 다양한 형태의 전기적 신호로 이루어져 있으며, 이러한 신호들의 특징점을 분석함으로써 부정맥을 검출할 수 있다. 지금까지 부정맥 검출을 위한 특징점 추출 방법에 대하여 많은 연구가 이루어졌으나, 복잡한 연산과정으로 실시간 연산 결과를 활용하는 휴대형 기기에는 부적합하다. 이와 같은 문제점을 해결하기 위하여 본 연구에서는 환자의 R-R 간격과 QRS 너비의 정보를 이용하여 R파를 추출하였다. 우선 버터워스 필터를 이용하여 저주파 대역의 잡음을 제거하였으며, R-R간격의 이동평균과 QRS 너비의 이동평균을 이용하여 R파를 추출하였다. 이에 대한 결과 검증은 MIT-BIH 부정맥 데이터베이스의 데이터를 활용하여 실험하였으며, 제공된 데이터의 R파 위치와 제안한 알고리즘의 R파 위치를 비교하였다. 이에 대한 결과로는 제안한 알고리즘 방법이 우수한 검출 성능을 보였으며, 연산과정에서도 효율적인 방법임을 확인 할 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제41권5호
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• pp.185-194
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• 2020

Arrhythmia is basically diagnosed with the electrocardiogram (ECG) signal, however, ECG is difficult to measure and it requires expert help in analyzing the signal. On the other hand, the radial pulse can be measured with easy and uncomplicated way in daily life, and could be suitable bio-signal for the recent untact paradigm and extensible signal for diagnosis of Korean medicine based on pulse pattern. In this study, we developed an irregular radial pulse detection algorithm based on a learning model and considered its applicability as arrhythmia screening. A total of 1432 pulse waves including irregular pulse data were used in the experiment. Three data sets were prepared with minimal preprocessing to avoid the heuristic feature extraction. As classification algorithms, elastic net logistic regression, random forest, and extreme gradient boosting were applied to each data set and the irregular pulse detection performances were estimated using area under the receiver operating characteristic curve based on a 10-fold cross-validation. The extreme gradient boosting method showed the superior performance than others and found that the classification accuracy reached 99.7%. The results confirmed that the proposed algorithm could be used for arrhythmia screening. To make a fusion technology integrating western and Korean medicine, arrhythmia subtype classification from the perspective of Korean medicine will be needed for future research.

• 한국지능시스템학회논문지
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• 제23권4호
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• pp.292-297
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• 2013

본 논문에서는 불응기(Refractory Period)에 기반한 부정맥 심전도 신호의 특이 파형 검출 알고리즘을 제안한다. 부정맥 심전도 신호는 대부분 평균 10% 정도의 특이 파형을 갖는다. 따라서 장시간 심전도 신호를 관찰 및 분석해야 하는 의료진에게 심전도 신호 샘플의 90%이상이 축소된 특이 파형만을 제공함으로써 시간과 비용 측면에서 매우 큰 효과를 볼 수 있다. 제안 알고리즘은 R-파의 특징과 가변 불응기를 이용하여 R-peak를 검출한다. 검출된 R-peak에 대해 특이 파형에 포함되지 않은 R-peak들의 전위 및 첨도의 평균과 표준편차를 이용하여 특이 파형을 검출한다. 제안한 알고리즘을 MIT-BIH 부정맥 데이터베이스의 모든 레코드에 적용한 결과 평균 90% 이상의 압축률을 보였다.

• 한국정보통신학회논문지
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• 제23권2호
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• pp.117-126
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• 2019

부정맥 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경망, 퍼지, 시계열 주파수 분석, 비선형 분석법 등이 연구되어 왔다. 이러한 방법들은 분류율를 향상시키기 위해 정확한 특징점과 많은 양의 신호를 처리해야 하기 때문에 데이터의 가공 및 연산이 복잡하며, 다양한 부정맥을 분류하는데 어려움이 있다. 본 연구에서는 AR(Auto Regressive) 모델링 기반의 특징점 추출과 SVM(Support Vector Machine)을 통한 조기수축 부정맥 분류 방법을 제안한다. 이를 위해 잡음을 제거한 ECG 신호에서 R파를 검출하고 QRS와 RR 간격의 특정 파형 구간을 모델링하였다. 이후 최적 세그먼트 길이(n1, n2), 최적 차수( p1, p2)의 4가지 AR 모델링 변수를 추출하고 SVM을 통해 Normal, PVC, PAC를 분류하였다. 연구의 타당성을 입증하기 위해 MIT-BIH 부정맥 데이터베이스를 대상으로 한 R파의 평균 검출 성능은 99.77%, Normal, PVC, PAC 부정맥은 각각 99.23%, 97.28, 96.62의 평균 분류율을 나타내었다.