• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.294-305
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• 2009

The bio signals essentially have different characteristics in each person. And the main purpose of automatic diagnosis algorithm based on bio signals focuses on discriminating differences of abnormal state from personal differences. In this paper, we propose automatic ECG diagnosis algorithm which discriminates normal heart beats from premature ventricular contraction using optimization of wavelet parameterization to solve that problem. The proposed algorithm optimizes wavelet parameter to let energy of signal be concentrated on specific scale band. We can reduce the personal differences and consequently highlight the differences coming from arrhythmia via this process. The proposed algorithm using ELM as a classifier show high discrimination performance between normal beat and PVC. From the experimental results on MIT-BIH arrhythmia database the performances of the proposed algorithm are 98.1% in accuracy, 93.0% in sensitivity, 96.4% in positive predictivity, and 0.8% in false positive rate. This results are similar or higher then results of existing researches in spite of small human intervention.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.110-113
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• 2005

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted fuzzy Membership Funcstions, NEWFM)을 이용하여 심전도 신호로부터 조기심실수축(Premature Ventricular Contraction, PVC)을 판별하는 퍼지규칙을 추출하고 있다. NEWFM은 자기적응적(self adaptive) 가중 퍼지소속함수를 가지고 주어진 입력 데이터로부터 학습하여 퍼지규칙을 생성하고 이를 기반으로 정상 파형과 PVC 파형을 구분한다. 분류 성능 평가를 위하여 MIT/BIH 부정맥 데이터 베이스를 사용하였으며, NEWFM의 입력은 심전도의 파형에 웨이블릿 변환을 적용하여 추출된 웨이블릿 계수를 사용하였다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 계수를 제거하면서 최소의 m 개 특징입력만을 사용한 하이퍼박스로 단순화 시킨다. 이러한 방법으로 추출된 2개의 웨이블릿 계수를 사용한 퍼지규칙은 $96\%$의 PVC 분류성능을 보여준다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.91-98
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• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.427-436
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• 2016

In general, QRS duration represent a distance of Q start and S end point. However, since criteria of QRS duration are vague and Q, S point is not detected accurately, arrhythmia classification performance can be reduced. In this paper, we propose extraction of Q, S start and end point RS feature based on phase transition tracking method after we detected R wave that is large peak of electrocardiogram(ECG) signal. For this purpose, we detected R wave, from noise-free ECG signal through the preprocessing method. Also, we classified QRS pattern through differentiation value of ECG signal and extracted Q, S start and end point by tracking direction and count of phase based on R wave. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 99.60%. PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction(PVC). The achieved scores indicate the average detection rate of 94.12% in PVC.

• The Korean Nurse
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• v.33 no.4
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• pp.62-85
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• 1994

The data were analyzed by using an S. P. S. S. computerized program for mean, standard deviation, percentage and paired t-test. The results of this study were as follows: 1. The increase in $PaO_2$ after hyperoxygenation and hyperinflation was highly statistically significant(p=0.041), and the increase in $PaO_2$ immediately after suctioning was not significant (p=0.752). The time of lowest $PaO_2$ was 30 seconds after the endotracheal suction. 2. The occurrance of cardiac arrhythmia after the endotracheal suction included sinus tachycardia, sinus arrhythmia, sinus bradycardia, premature atrial contraction (PAC), and premature ventricular contraction (PVC). The most frequent cardiac arrhythmia was sinus tachycardia (a subjects). Sinus arrhythmia was observed in 5 subjects and continued till 10 minutes after suctioning in two of these. Sinus bradycardia occurred in only 3 subjects and among them, 1 subjects shows sinus arrythmia till 10 minutes after suctioning along. PAC was observed in only one subject and continued till five minutes after suctining along with sinus arrhythmia. PVC was observed in three subjects: it lasted for only 30 seconds after suctioning in two subjects. but continued for 10 minutes after suctioning in the third. 6 subjects manifested two kinds of Cardiac arrhythmia Three of them showed sinus tachycardia with PVC, another 2 showed sinus bradycardia with sinus arrhythmia, and the other subject showed sinus arrhythmia with PAC. 3. The increases in heart rate during the endotracheal suction immediately after and at 30 seconds after suctioning were statistically significant (p=0.005). The increase in heart rate at one minute after suctioning was also significant (p=0.023). The increase in heart rate continued until 10 minutes after the endotracheal suction, but was not statistically significant In this study, endotracheal suctioning with hyperoxygenation and hyperinflation was effective in preventing a decrease in $PaO_2$ after suctioning, but not in preventing cardiac arrhythmias. Nurses should be aware of the complications of endotracheal suctioning and do effective hyperoxygenation and hyperinflation before and after suctioning. Further research is needed to develop a efficient endotracheal suction method which will minimize complications. This study needs to be replicated with different population of patients intubatted or having a tracheostomy, specifically, patients who cardiac or pulmonary desease. The data were analyzed by using an S. P. S. S. computerized program for mean, standard deviation, percentage and paired t-test. The results of this study were as follows: 1. The increase in $PaO_2$ after hyperoxygenation and hyperinflation was highly statistically significant(p=0.041), and the increase in $PaO_2$ immediately after suctioning was not significant (p=0.752). The time of lowest $PaO_2$ was 30 seconds after the endotracheal suction. 2. The occurrance of cardiac arrhythmia after the endotracheal suction included sinus tachycardia, sinus arrhythmia, sinus bradycardia, premature atrial contraction (PAC), and premature ventricular contraction (PVC). The most frequent cardiac arrhythmia was sinus tachycardia (a subjects). Sinus arrhythmia was observed in 5 subjects and continued till 10 minutes after suctioning in two of these. Sinus bradycardia occurred in only 3 subjects and among them, 1 subjects shows sinus arrythmia till 10 minutes after suctioning along. PAC was observed in only one subject and continued till five minutes after suctining along with sinus arrhythmia. PVC was observed in three subjects: it lasted for only 30 seconds after suctioning in two subjects. but continued for 10 minutes after suctioning in the third. 6 subjects manifested two kinds of Cardiac arrhythmia Three of them showed sinus tachycardia with PVC, another 2 showed sinus bradycardia with sinus arrhythmia, and the other subject showed sinus arrhythmia with PAC. 3. The increases in heart rate during the endotracheal suction immediately after and at 30 seconds after suctioning were statistically significant (p=0.005). The increase in heart rate at one minute after suctioning was also significant (p=0.023). The increase in heart rate continued until 10 minutes after the endotracheal suction, but was not statistically significant In this study, endotracheal suctioning with hyperoxygenation and hyperinflation was effective in preventing a decrease in $PaO_2$ after suctioning, but not in preventing cardiac arrhythmias. Nurses should be aware of the complications of endotracheal suctioning and do effective hyperoxygenation and hyperinflation before and after suctioning. Further research is needed to develop a efficient endotracheal suction method which will minimize complications. This study needs to be replicated with different population of patients intubatted or having a tracheostomy, specifically, patients who cardiac or pulmonary desease.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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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.

• Industrial Engineering and Management Systems
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• v.12 no.4
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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.

• Journal of Biomedical Engineering Research
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• v.20 no.4
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• pp.435-442
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• 1999

A fuzzy-ART(adaptive resonance theory) network for the PVC(premature ventricular contraction) classification using wavelet coefficient is designed. This network consists of the feature extraction and learning of the fuzzy-ART network. In the first step, we have detected the QRS from the ECG signal in order to set the threshold range for feature extraction and the detected QRS was divided into several frequency bands by wavelet transformation using Haar wavelet. Among the low-frequency bands, only the 6th coefficient(D6) are selected as the input feature. After that, the fuzzy-ART network for classification of the PVC is learned by using input feature which comprises of binary data converted by applying threshold to D6. The MIT/BIH database including the PVC is used for the evaluation. The designed fuzzy-ART network showed the PVC classification ratio of 96.52%.

• 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.

• Biomolecules & Therapeutics
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• v.13 no.1
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• pp.20-25
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• 2005

The present study was performed to evaluate antiarrhythmic effects of KR-32570, a novel inhibitor of sodium hydrogen exchanger subtype-1 (NHE-1), in rat arrhythmia induced by focal ischemia and reperfusion. During ischemia, KR-32570 significantly decreased the number of premature ventricular contraction (PVC) from 152.0 times to 75.5, 52.4 and 20.0 times for 0.1, 0.3 and 1.0 mg/kg, respectively (p<0.05) and the duration of ventricular tachycardia (VT) from 88.1 s to 35.8, 7.7 and 1.3 s, respectively(p<0.05) in anesthetized rats subjected to 10-min coronary occlusion of coronary artery. Similarlt to ischemia-induced arrhythmia, KR-32570 significantly decreased reperfusion-induced arrhythmia including PVC (41.3, 21.5, 11.3 and 6.6 times at vehicle, 0.1, 0.3 and 1.0 mg/kg, respectively, p<0.05) and VT (100.5, 64.2, 25.8 and 25.2 s, respectively, p<0.05), and VF (86.9, 27.5, 6.9 and 0 s, respectively, p<0.05). Moreover, KR-32570 dose-dependently decreased the incidence of mortality occurring after reperfusion (41, 27, 18 and 0% at vehicle, 0.1, 0.3, 1.0 mg/kg, respectively). These results suggest that KR-32570 has a potent antiarrhythmic effect in rat arrhythmia induced by ischemia and reperfusion.