• Journal of Industrial Technology
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• v.22 no.B
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• pp.35-44
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• 2002

To find the optimal solution as rapidly and exactly as possible with Nelder-Mead simplex algorithm, the present values of the reflection, expansion, contraction and/or shrink parameters of this algorithm are needed to be changed at appropriate time during the search process. The reflection parameter is selected in this study in order to be changed because reflection, expansion and contraction process can be simultaneously effected by only this parameter. Two independent indices for determining whether the present value of the reflection parameter of this algorithm should be changed or not during the search process are suggested in this study. Those indices were made of the equations of Nelder-Mead simplex algorithm's convergence criterion and Dennis-Wood's convergence criterion, respectively. It is appeared that the optimal solution can be find with smaller numbers of objective function evaluation than the original Nelder-Mead's one with fixed parameter when the those indices are used during the search process. and the more remarkable reduction effect of the number of an objective function evaluation can be obtained when the latter index is used.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.437-444
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• 2016

Most methods for detecting arrhythmia require pp interval, diversity of P wave morphology, but it is difficult to detect the p wave signal because of various noise types. Therefore it is necessary to use noise-free R wave. In this paper, we propose algorithm for premature contraction arrhythmia classification through ECG pattern analysis and template threshold. For this purpose, we detected R wave through the preprocessing method using morphological filter, subtractive operation method. Also, we developed algorithm to classify premature contraction wave pattern using weighted average, premature ventricular contraction(PVC) and atrial premature contraction(APC) through template threshold for R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 6 record of MIT-BIH arrhythmia database that included over 30 PVC and APC. The achieved scores indicate the average of 99.77% in R wave detection and the rate of 94.91%, 95.76% in PVC and APC classification.

• 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).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1072-1077
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• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• 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(%).

• Journal of Korea Multimedia Society
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• v.12 no.3
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• pp.451-459
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• 2009

This paper presents an approach to detect premature ventricular contraction(PVC) using discrete wavelet transform and fuzzy neural network. As the input of the algorithm, we use 14 coefficients of d3, d4, and d5, which are transformed by a discrete wavelet transform(DWT). This paper uses a neural network with weighted fuzzy membership functions(NEWFM) to diagnose PVC. The NEWFM discussed in this paper classifies a normal beat and a PVC beat. The size of the window of DWT is $-31/360{\sim}+32/360$ second(64 samples) whose center is the R wave. Using the seven records of the MIT-BIH arrhythmia database used in Shyu's paper, the classification performance of the proposed algorithm is 99.91%, which outperforms the 97.04% of Shyu's analysis. Using the forty records of the M1T-BIH arrhythmia database used in Inan's paper, the classification performance of the proposed algorithm is 98.01%, which outperforms 96.85% of Inan's one. The SE and SP of the proposed algorithm are 84.67% and 99.39%, which outperforms the 82.57% and 98.33%, respectively, of Inan's study.

• Korean Journal of Mathematics
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• v.20 no.2
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• pp.225-237
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• 2012

In this paper, we introduce a new iterative scheme to investigate the problem of nding a common element of nonexpansive mappings and the set of solutions of generalized variational inequalities for a $k$-strict pseudo-contraction by relaxed extra-gradient methods. Strong convergence theorems are established in $q$-uniformly smooth Banach spaces.

• Kyungpook Mathematical Journal
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• v.62 no.1
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• pp.69-88
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• 2022

In this work, the three-step intermixed iteration for two finite families of nonlinear mappings is introduced. We prove a strong convergence theorem for approximating a common fixed point of a strict pseudo-contraction and strictly pseudononspreading mapping in a Hilbert space. Some additional results are obtained. Finally, a numerical example in a space of real numbers is also given and illustrated.

• Journal of IKEEE
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• v.12 no.2
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• pp.87-94
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• 2008

Pelvic floor muscle is the main subsystem that maintains urinary continence. It is possible to diagnose the degree of the stress urinary incontinence(SUI) by evaluating the contraction pressure of the pelvic floor muscle. Bio-signal measurement system was developed to measure the contraction pressure. Diagnostic parameters were drawn out by analyzing the measured data. Statistical evaluations were done to classify the all subjects with five groups each has similar characteristics. SUI diagnostic algorithm was implemented to each group separately. The accuracy of the algorithm was about 78.9% and utility was confirmed by clinical trial.

• Nonlinear Functional Analysis and Applications
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• v.28 no.1
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• pp.175-203
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• 2023

In this paper, we study an iterative algorithm that is based on inertial proximal and contraction methods embellished with relaxation technique, for finding common solution of monotone variational inclusion, and fixed point problems of pseudocontractive mapping in real Hilbert spaces. We establish a strong convergence result of the proposed iterative method based on prediction stepsize conditions, and under some standard assumptions on the algorithm parameters. Finally, some special cases of general problem are given as applications. Our results improve and generalized some well-known and related results in literature.