• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

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 accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Proceedings of the KOSOMBE Conference
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• v.1989 no.05
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• pp.15-18
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• 1989

This paper presents a new algorithm for P-wave detection in the ECG signal. We detect the peak and valley point using significant point extraction algorithm with 9-point derivative. Because P-wave duration is changed according to heart-rates, we search for the R-peak and calculate the R-R interval time prior to the determination of P-wave duration threshold values in order to actively adapt to the change of P duration. We determine the parameters for P-wave detection and then P-peak, P-onset and P-offset are detected by these parameters. The results obtained from the proposed algorithm have detected successively P-wave almost more than 90%.

• Journal of the Korean earth science society
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• v.38 no.3
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• pp.173-181
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• 2017

The stability of the steady Rossby-Haurwitz wave (R-H wave) in the nondivergent barotropic model (NBM) on the sphere was investigated with the normal mode method. The linearized NBM equation with respect to the R-H wave was formulated into the eigenvalue-eigenvector problem consisting of the huge sparse matrix by expanding the variables with the spherical harmonic functions. It was shown that the definite threshold R-H wave amplitude for instability could be obtained by the normal mode method. It was revealed that some unstable modes were stationary, which tend to amplify without the time change of the spatial structure. The maximum growth rate of the most unstable mode turned out to be in almost linear proportion to the R-H wave amplitude. As a whole, the growth rate of the unstable mode was found to increase with the zonal- and total-wavenumber. The most unstable mode turned out to consist of more-than-one zonal wavenumber, and in some cases, the mode exhibited a discontinuity over the local domain of weak or vanishing flow. The normal mode method developed here could be readily extended to the basic state comprised of multiple zonalwavenumber components as far as the same total wavenumber is given.

• Journal of IKEEE
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• v.25 no.1
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.945-952
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• 2013

The R wave of QRS complex is the most prominent feature in ECG because of its specific shape; therefore it is taken as a reference in ECG feature extraction. But R wave detection suffers from the fact that frequency bands of the noise/other components such as P/T waves overlap with that of QRS complex. ECG signal processing must consider efficiency for hardware and software resources available in processing for miniaturization and low power. In other words, the design of algorithm that exactly detects QRS region using minimal computation by analyzing the person's physical condition and/or environment is needed. Therefore, efficient QRS detection based on SOM(Subtractive Operation Method) is presented in this paper. For this purpose, we detected R wave through the preprocessing method using morphological filter, empirical threshold, and subtractive signal. Also, we applied dynamic backward searching method for efficient detection. The performance of R wave detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41% in R wave detection.

• Kyungpook Mathematical Journal
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• v.18 no.2
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• pp.311-319
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• 1978

This paper deals with the evaluation of two definite integrals involving spheroidal wave function, H-function of two variables, and the generalized hypergeometric function. Also, an expansion formula for the product of generalized hypergeometric function and the H-function of two variables has been obtained. Since the H-function of two variables, spheroidal wave functions, and the generalized hypergeometric function may be transformed into a number of higher transcendental functions and polynomials, the results obtained in this paper include some known results as their particular cases. As an application of such results, a problem of heat conduction in a non-homogenous bar has been solved by using the generalized Legendre transform [9].

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.462-463
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• 2006

본 논문에서는 당사에서 개발한 Digital Relay 및 Meter와 같은 IED들의 고장/이벤트 파형을 분석하는 툴인 Wave Analyzer를 소개한다. 기존 당사 파형 분석 툴들은 각기 하나의 IED에 대해서만 적용 가능하였으나 Wave Analyzer는 당사의 모든 IED에 대해 적용 가능하도록 통합형으로 개발하였다. 또한 고조파 분석, 벡터 다이어그램 등 파형 분석에 필요한 다양한 기능을 제공한다. Wave Analyzer는 다양한 기능을 가지고 있으며 모든 IED에 적용할 수 있어 당사 IED를 사용하는 사용자들에게 보다 편리함과 유용함을 제공할 것이다.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.48-50
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• 2006

In this paper, we propose a method to minimize the baseline wandering that make hard to detect R wave in ECG. This method uses a different signal between ECG and ascending slope tracing waves to minimize the baseline wandering. When the slope of ECG signal maintains the value or falls, the ascending slope tracing wave fellows ECG signal directly, and this wave holds that value of ECG signal when the slope begins to rises in a certain time(=hold time). After this hold time, this wave traces ECG signal again. To evaluate this minimizing method for baseline wandering, the experiments are carried out with 5 ECG data in the database of MIT/BIH. R waves in the proposed different signal are detected by using descending slope trace waves and compared with the annotation file. The results show that the proposed method Is sure to minimize the baseline wandering in ECG.

• Journal of Advanced Research in Ocean Engineering
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• v.1 no.4
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• pp.239-251
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• 2015

In this study, a spectral fatigue analysis was performed for the topside structure of an offshore floating vessel. The topside structure was idealized using beam elements in the SACS program. The fatigue analysis was carried out considering the wave and wind loads separately. For the wave-induced fatigue damage calculation, motion RAOs calculated from a direct wave load analysis and regular waves with different periods and unit wave heights were utilized. Then, the member end force transfer functions were generated covering all the loading conditions. Stress response transfer functions at each joint were produced using the specified SCFs and member end force transfer functions. fatigue damages were calculated using the obtained stress ranges, S-N curve, wave spectrum, heading probability of each loading condition, and their corresponding occurrences in the wave scatter diagrams. For the wind induced fatigue damage calculation, a dynamic wind spectral fatigue analysis was performed. First, a dynamic natural frequency analysis was performed to generate the structural dynamic characteristics, including the eigenvalues (natural frequencies), eigenvectors (mode shapes), and mass matrix. To adequately represent the dynamic characteristic of the structure, the number of modes was appropriately determined in the lateral direction. Second, a wind spectral fatigue analysis was performed using the mode shapes and mass data obtained from the previous results. In this analysis, the Weibull distribution of the wind speed occurrence, occurrence probability in each direction, damping coefficient, S-N curves, and SCF of each joint were defined and used. In particular, the wind fatigue damages were calculated under the assumption that the stress ranges followed a Rayleigh distribution. The total fatigue damages were calculated from the combination with wind and wave fatigue damages according to the DNV rule.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1420-1428
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• 2017

It is difficult to guarantee the reliability of the algorithm due to the difference of the sampling frequency among the various ECG databases used for the R wave detection in case of applying to different environments. In this study, we propose an optimal threshold setting method for R wave detection according to the sampling frequency of ECG signals. For this purpose, preprocessing process was performed using moving average and the squaring function based the derivative. The optimal value for the peak threshold was then detected according to the sampling frequency by changing the threshold value according to the variation of the signal and the previously detected peak value. The performance of R wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. When the optimal values of the differential section, window size, and threshold coefficient for the MIT-BIH sampling frequency of 360 Hz were 7, 8, and 6.6, respectively, the R wave detection rate was 99.758%.