• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1924-1925
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• 2007

본 논문에서는 부정맥 판별을 위한 전처리 과정으로 PCA, LDA, ICA를 바탕으로 하여 정확도를 비교하여 보았다. 각각의 전처리는 고유의 특성을 가지고 있으며 본 논문의 목적은 부정맥 판별상 어떤 전처리가 더욱 정확성의 면에서 효과적인지를 알아보는 것이다. 본 논문의 데이터는 MIT-BIH에 기반하고 있으며, Beat의 분류는 정상(Normal), 좌각차단(Left Bundle Branch Block, LBBB), 우각차단(Right Bundle Branch Block, RBBB), 조기심실수축(Premature Ventricular Contraction, PVC), 조기심방수축(Atrial Premature Beat, APB), paced Beat, 심실보충수축(Ventricular Escape Beat)로 나누었다. 실험적 결과는 PCA-BPNN의 경우 95.53%, ICA-BPNN의 경우 93.95%, LDA-BPNN의 경우 96.42%로 LDA가 가장 ECG 부정맥 판별 응용에 있어 가장 효율적인 방법으로 나타났다.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.571-579
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• 2005

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

• 한국정보통신학회논문지
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• 제14권1호
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• pp.16-22
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• 2010

심전도 신호 분석 및 부정맥 분류는 환자를 진단하고 치료하는데 중요한 역할을 한다. 부정맥은 맥박이 불규칙한 상태로 심실빈맥(VT)이나 심실세동(VF) 환자에게 심각한 위협이 될 수 있다. 심방조기수축(APC)과 상심실성빈맥(SVT), 심실조기수축(PVC)은 심실빈맥(VT)만큼 치명적이지는 않지만 심장질환을 진단하는데 중요한 부정맥이다. 본 논문은 2~3개의 부정맥 분류만을 고려한 기존의 방법을 극복하고 다양한 부정맥을 분류하기 위한 새로운 방법을 제시한다. 심전도 신호의 특징 추출을 위해서 EMD 방법으로 신호를 분해하여 IMFs를 얻는다. 입력 데이터의 양은 분류기 성능에 영향을 미치므로 신호 데이터의 차원을 감소시키기 위해 Burg 알고리즘을 IMFs에 적용하여 AR 계수를 구하고 여러 개의 이진 분류기를 결합한 다중 클래스 SVM의 입력으로 사용한다. 최적의 SVM 성능 파라미터를 선택하고 부정맥 분류에 적용한 결과 검출의 정확성은 96.8%~99.5%였다. 실험 결과는 제안한 EMD 방법에 의한 전처리 및 특징 추출과 다중 클래스 SVM에 의한 부정맥 분류의 유용성을 보여준다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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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.

• Clinical and Experimental Pediatrics
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• 제53권5호
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• pp.644-647
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• 2010

Purpose: To evaluate myocardial conductivity to understand cardiac involvement in patients with mitochondrial disease. Methods: We performed retrospective study on fifty-seven nonspecific mitochondrial encephalopathy patients with no clinical cardiac manifestations. The patients were diagnosed with mitochondrial respiratory chain complex defects through biochemical enzyme assays of muscle tissue. We performed standard 12-lead electrocardiography (ECG) on all patients. Results: ECG abnormalities were observed in 30 patients (52.6%). Prolongation of the QTc interval (>440 ms) was seen in 19 patients (33.3%), widening of the corrected QRS interval in 15 (26.3%), and bundle branch block in four (7.0%). Atrioventricular block, premature atrial contraction and premature ventricular contraction were seen in two patients each (3.5%) and Wolff-Parkinson-White syndrome in one patient (1.8%). Conclusion: Given this finding, we recommend active screening with ECG in patients with mitochondrial disease even in patients without obvious cardiac manifestation.

• 센서학회지
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• 제29권1호
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• pp.19-26
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• 2020

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

• 한국정보통신학회논문지
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• 제23권12호
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• pp.1542-1550
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• 2019

부정맥 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경회로망(Artificial Neural Network), 퍼지(Fuzzy), 기계학습(Machine Learning) 등을 이용한 방법이 연구되어 왔다. 특히 딥러닝은 신경회로망의 문제인 은닉층 개수의 한계를 해결함으로 인해 오류 역전파 알고리즘을 이용한 부정맥 분류에 가장 많이 사용되고 있다. 딥러닝 모델을 심전도 신호에 적용하기 위해서는 적절한 모델선택과 파라미터를 최적에 가깝게 선택할 필요가 있다. 본 연구에서는 심실 조기 수축 비트 검출을 위한 딥러닝 기반의 최적 파라미터 검출 방법을 제안한다. 이를 위해 먼저 잡음을 제거한 ECG신호에서 R파를 검출하고 QRS와 RR간격 세그먼트를 추출하였다. 이후 딥러닝을 통한 지도학습 방법으로 가중치를 학습시키고 검증데이터로 모델을 평가하였다. 제안된 방법의 타당성 평가를 위해 MIT-BIH 부정맥 데이터베이스를 통해 각 파라미터에 따른 딥러닝 모델로 훈련 및 검증 정확도를 확인하였다. 성능 평가 결과 R파의 평균 검출 성능은 99.77%, PVC는 97.84의 평균 분류율을 나타내었다.

• Industrial Engineering and Management Systems
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• 제12권4호
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• pp.380-388
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• 2013

Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

• 한국임상수의학회지
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• 제6권2호
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• pp.281-290
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• 1989

To gain insight into the relationship between the occurrence of occlusive arrhythmia(OA) and the incidence of reperfusion arrhythmia(RA), this study used 25 open-chest dogs anesthetized with halothan, these were ligated between anterior ventricular branch and marginal branch of left circumflex artery for 30 minutes and occlusive arrhythmia were observed during the ligation. After releasing of the ligation, TA were observed during 5 minutes. The results were summerized as follow； 1. Such arrhythmias as ventricular fibrillation(VF), short run type VPC Premature contraction(VPC), Venticular tachycardia(VT), ventricularc and trigeminy VPC(TVPC) were observed during occlusion and reperfusion. 2. The cases occurred VT, SRVPC and TVPC during occlusion necessarily were Incidence of RA. 3. RA never occurred without appearence of occlusive arrhythmias. 4. The occurrence rate of OA showed 55.5% in the incidence group of RA and 24.6％ in the non incidence group of RA. 5. The occurrence rate of VPC during occlusion showed 9.9＋5.85(episode/min) in the incidence group of RA and 4.46＋5.88(episode/min) in the non-incidence group of RA. These results may be estimated that the occurrence of VT, SRVPC and TVPC, and the high occurrence rate of VPC during occlusion can be predicted the incidence.

• 전기학회논문지
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• 제60권12호
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• pp.2380-2385
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• 2011

With the advent of ubiquitous healthcare technology to provide a patient with the necessary medical services in anywhere and anytime scheme, the importance of securing safe communication without tampering the medical data by the unauthorized users is getting more emphasized. With this aim, a novel method for constructing encryption keys on the basis of biometrical measurement of electrocardiogram (ECG) is suggested in this study. The experiments on MIT/BIH database show that our proposed method can achieve safe communication by successfully ciphering and deciphering ECG data including premature ventricular contraction arrhythmia signal with compromising its fiducial features as biometric key to transmit the data via the internet network.