• Journal of Chest Surgery
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• v.33 no.10
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• pp.777-784
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• 2000

Congenital aortic stenosis in children is characterized by "excessive" left ventricular hypertrophy with reduced left ventricular systolic wall stress that allows for supernormal ejection performance. We hypothesized that left ventricular wall stress was decreased immediately after surgical correction of pure congenital aortic stenosis. Also measuring postoperative left ventricular wall stress was a useful noninvasive measurement that allowed direct assessment for oxygen consumption of myocardium than measuring the peak systolic pressure gradient between ascending aorta and left ventricle for the assessment of surgical results. Material and Method: Between September 1993 and August 1999, 8 patients with isolated congenital aortic stenosis who underwent surgical correction at Yonsei cardiovascular center were evaluated. There were 6 male and 2 female patients ranging in age from 2 to 11 years(mean age, 10 years). Combined Hemodynamic-Ultrasonic method was used for studying left ventricular wall stress. We compared the wall stress peak systolic pressure gradient and ejection fraction preoperatively and postoperatively. Result: After surgical correction peak aortic gradient fell from 58.4${\pm}$17.6, to 23.7${\pm}$17.7 mmHg(p=0.018) and left ventricular ejection fraction decreased but it is not statistically significant. In the consideration of some factors that influence left ventricular end-systolic wall stress excluding one patient who underwent reoperation for restenosis of left ventricular outflow tract left ventricular end-systolic pressure and left ventricular end-systolic dimension were fell from 170.6${\pm}$24.3 to 143.7${\pm}$27.1 mmHg and from 1.78${\pm}$0.4 to 1.76${\pm}$0.4 cm respectively and left ventricular posterior wall thickness was increased from 1.10${\pm}$0.2, to 1.27${\pm}$0.3cm but it was not statistically singificant whereas left ventricular end-systolic wall stress fell from 79.2${\pm}$24.9 to 57.1${\pm}$27.6 kdynes/cm2(p=0.018) in 7 patients. For one patient who underwent reoperation peak aortic gradient fell from 83.0 to 59.7 mmHg whereas left ventricular end-systolic wall stress increased from 67.2 to 97.0 kdynes/cm2 The intervals did not change significnatly. Conclusion ; We believe that probably some factors that are related to left ventricular geometry influenced the decreased left ventricular wall stress immediately after surgical correction of isolated congenital aortic stenosis. Left ventricular wall stress is a noninvasive measurement and can allow for more direct assesment than measuring peak aortic gradient particularly in consideration of the stress and oxygen consumption of the myocardium therefore we can conclude it is a useful measurement for postoperative assessment of congenital aortic stenosis.

• The Korean Journal of Nuclear Medicine
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• v.19 no.2
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• pp.43-50
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• 1985

With the improvement of hemodialysis, the course of the disease in patient with endstage renal disease has been clearly improved. Nevertheless, among several shortcomings to our present mode of renal replacement therapy, cardiovascular complications have been the leading cause of morbidity and mortality. Several factors such as anemia, arteriovenous shunting of blood, intermittent extracorporeal circulation and hypertension may be contributing. But little is known about the quantitative cardiac hemodynamic characteristics occurred during hemodialysis. The purpose of this study is to observe the sequential hemodynamic changes before, during and after the hemodialysis and to investigate reliable parameters in the detection of ventricular dysfunction. In the present study, equilibrium radionuclide cardiac angiography was performed and left and right ventricular volume indices, ejection phase indices of both ventricular, performance were measured in the 16 stable patients with chronic renal failure treated with maintenance hemodialysis sequentially i.e. before, during (early and late phase) and after the hemodialysis. The results obtained were as follows; 1) The indices of the left ventricular function were not changed during the hemodialysis but increased after the hemodialysis. 2) The indices of the right ventricular function(EF, SVI) were significantly decreased in the early phase (15, 30 minutes after starting extracorporeal circulation) but recovered after the hemodialysis. 3) The ratio of right ventricular to left ventricular ejection fraction was significantly decreased in the early phase and the lung volume indices were significantly increased at the same phase. As a conclusion, hemodialysis improves left ventricular function maybe due to increased contractility, and effects on the right ventricular function maybe due to the increased lung volume in the early phase of hemodialysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.988-1001
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• 2018

Arrhythmia has recently emerged as one of the major causes of death in Koreans. Premature Ventricular Contraction (PVC) is the most common arrhythmia that can be found in clinical practice, and it may be a precursor to dangerous arrhythmias, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect an abnormal heart beat and diagnose arrhythmia early. We extracted the features corresponding to the QRS pattern from the subject's ECG signal and classify the premature ventricular contraction waveform using the features. We modified the weighting and bias values based on the error back-propagation algorithm through learning data. We classify the normal signal and the premature ventricular contraction signal through the modified weights and deflection values. MIT-BIH arrhythmia data sets were used for performance tests. We used RR interval, QS interval, QR amplitude and RS amplitude features. And the hidden layer with two nodes is composed of two layers to form a total three layers (input layer 0, output layer 3).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.260-266
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• 1996

In this paper, we presents neural network identification and control of highly complicated nonlinear Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Generally the LVAD system need to compensate nonlinearities. Hence, it is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with Neural Network Identification. Once the NNI has learned the dynamic model of LVAD system, the other network, called Neural Network Controller(NNC), is designed for control of a LVAD system. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.315-320
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• 1998

In this paper, we present the PID control method for the controlling flow rate of highly complicated nonlinear Left Ventricular Assist Device(LVAD) with pneumatically driven mock circulatory system. Beat Rate (BR), Systole-Diastole Rate (SDR) and flow rate are used as the main variables of the LVAD system. System modeling is completed using the neural network with input variables (BR, SDR, their derivatives, actual flow) and an output valiable(actual flow). Then, as the basis of this model, we perform the simulation of PID control to predict the performance and tendency of the system and control the flow rate of LVAD system using the PID controller. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated through computer simulation and experiments.

• Journal of Sensor Science and Technology
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• v.22 no.5
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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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.7-14
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• 2021

With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.

• Proceedings of the KAIS Fall Conference
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• 2011.12a
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• pp.170-173
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• 2011

Therapeutic hypothermia(TH) improves neurological outcomes and reduces mortality among survivors of out-of-hospital cardiac arrest. Animal and human studies have shown that TH results in improved salvage of the myocardium, reduced infarct size, reduced left ventricular remodeling and better long-term left ventricular function in settings of regional myocardial ischemia. This study is to investigate the effect of TH on post-resuscitation myocardial dysfunction and survival time after cardiac arrest and resuscitation in a rat model of myocardial infarction (MI). Thoracotomies were performed in 10 Male Sprague-Dawley rats weighing 450-550 g. MI was induced by ligation of the left anterior descending coronary artery (LAD). Ninety min after LAD ligation, ventricular fibrillation induction and subsequent cardiopulmonary resuscitation was performed before defibrillation attempts. Animals were randomized to two groups: a) Acute MI-Normothermia b) Acute MI-Hypothermia ($32^{\circ}C$ for 4 h). Myocardial functions, including cardiac output, left ventricular ejection fraction, and myocardial performance index were measured echocardiographically together with duration of survival. Ejection fraction, cardiac output and myocardial performance index were $54.74{\pm}9.16$, $89.00{\pm}8.89$, $1.30{\pm}0.09$ respectively and significantly better in the TH group than those of the normothermic group at the first 4 h after resuscitation($32.20{\pm}1.85$,$41.60{\pm}8.62$,$1.77{\pm}0.19$)(p=0.00). The survival time of the hypothermic group ($31.8{\pm}14.8$ h) was greater than that of the normothermic group($12.3{\pm}6.5$ h, p<0.05). This study suggested that TH attenuated post resuscitation myocardial dysfunction in acute MI and would be a potential strategy in post resuscitation care.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.27-34
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• 2005

Ventricular fibrillation(VF) is generally caused by chaotic behavior of electrical propagation in heart and may result in sudden cardiac death. In this study, we proposed a ventricular fibrillation detection algorithm based on support vector machine classifier, which could offer benefits to reduce the teaming costs as well as good classification performance. Before the extraction of input features, raw ECG signal was applied to preprocessing procedures, as like wavelet transform based bandpass filtering, R peak detection and segment assignment for feature extraction. We selected input features which of some are related to the rhythm information and of others are related to wavelet coefficients that could describe the morphology of ventricular fibrillation well. Parameters for SVM classifier, C and ${\alpha}$, were chosen as 10 and 1 respectively by trial and error experiments. Each average performance for normal sinus rhythm ventricular tachycardia and VF, was 98.39%, 96.92% and 99.88%. And, when the VF detection performance of SVM classifier was compared to that of multi-layer perceptron and fuzzy inference methods, it showed similar or higher values. Consequently, we could find that the proposed input features and SVM classifier would one of the most useful algorithm for VF detection.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.204-210
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• 2011

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.