• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.93-101
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• 2013

The accurate detection of R-wave is important for other feature extraction in ECG, and R-wave has a lot of medical information about heart. Numerous R-wave detection algorithms have been studied on the ECG signal shape analysis, but it was difficult to find accurate R-wave when the shape of R-wave is similar to the shape of P-wave. This paper presents an R-wave detection method based on the refractory period that is the period of depolarization and repolarization of the cell membrane after excitation. And we also use the shape of kurtosis in the refractory period. The proposed method is validated using the ECG records of the MIT-BIH arrhythmia database. Experimental results show that the proposed method significantly outperforms other method in case of 105 and 108 record that have R-wave similar to P-wave, as well as other records.

• 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.

• 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.

• Steel and Composite Structures
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• v.17 no.6
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• pp.891-906
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• 2014

This study aims to present a three dimensional finite element model to investigate the wave propagation in a concrete filled steel tubular column (CFSC) due to transient impact load. Both the concrete and steel are regarded as linear elastic material. The impact load is simulated by a semi sinusoidal impulse. Besides the CFSC models, a concrete column (CC) model is established for comparing under the same loading condition. The propagation characteristics of the transient waves in CFSC are analyzed in detail. The results show that at the intial stage of the wave propagation, the velocity waves in CFSC are almost the same as those in CC before they arrive at the steel tube. When the waves reach the column side, the velocity responses of CFSC are different from those of CC and the difference is more and more obvious as the waves travel down along the column shaft. The travel distance of the wave front in CFSC is farther than that in CC at the same time. For different wave speeds in steel and concrete material, the wave front in CFSC presents an arch shape, the apex of which locates at the center of the column. Differently, the wave front in CC presents a plane surface. Three dimensional effects on top of CFSC are obvious, therefore, the peak value and arrival time of incident wave crests have great difference at different locations in the radial direction. High-frequency waves on the waveforms are observed. The time difference between incident and reflected wave peaks decreases significantly with r/R when r/R < 0.6, however, it almost keeps constant when $r/R{\geq}0.6$. The time duration between incident and reflected waves calculated by 3D FEM is approximately equal to that calculated by 1D wave theory when r/R is about 2/3.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.483-486
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• 2000

심전도에서 QRS complex와 R-wave의 검출은 부정맥 진단, 심전도의 특성점 검출 기준, heart rate variability(HRV) 측정에 있어서 중요하나, 시시각각 변화하는 생리적 변화와 여러 가지 노이즈로 인해 검출이 쉽지 않다 제안된 알고리듬에서는 wavelet filter banks를 이용하여 대칭적 enhanced 신호와 noise 와 같은 very high frequency 성분이 제거된 ECG에 근사화 된 approximated 신호를 얻는다. Enhanced 신호로부터 QRS complex의 위치를 검출하고, 검출된 위치의 주변에서 대칭적 wavelet의 특성이 반영된 dominant한 peak의 위치정보, 즉 R wave의 후보점을 얻는다. 이 위치 정보를 이용하여 enhanced 신호에서 각 peak에서의 크기, approxi-mated 신호에서 각 peak 주변에서의 기울기 변화, 기울기 부호 등을 고려하여 R-wave의 위치를 원래의 ECG 신호에서 얻는다. MIT/BIH database에 적용한 결과 99.6%의 QRS complex검출률과 92.9%의 R-wave 검출률을 보였다.

• Steel and Composite Structures
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• v.46 no.1
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• pp.133-141
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• 2023

Based on the third-order shear deformation theory, the wave propagations in doubly curved spherical- and cylindrical- panels reinforced by carbon nanotubes (CNTs) are firstly investigated in present work. The coupled equations of wave propagation for the carbon nanotubes reinforced composite (CNTRC) doubly curved panels are established. Then, combined with the harmonic balance method, the eigenvalue technique is adopted to simulate the velocity-wave number curves of the CNTRC doubly curved panels. In the end, numerical results are showed to discuss the effects of the impact of key parameters including the volume fraction, different shell types (including spherical (R₁=R₂=R) and cylindrical (R₁=R, R₂=→∞)), wave number as well as modal number on the sensitivity of elastic waves propagating in CNTRC doubly curved shells.

• 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 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1289-1295
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• 2009

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 prevention of possible life threatening cardiac diseases. Particularly, in the healthcare system that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. In other words, design of algorithm that exactly detects R wave using minimal computation and classifies PVC is needed. So, R wave detection algorithm based adaptive threshold and window for the classification of PVC is presented in this paper. For this purpose, ECG signals are first processed by the usual preprocessing method and R wave was detected and adaptive window through R-R interval is used for efficiency of the detection. The performance of R wave detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate 99.33%, 88.86% accuracy respectively for R wave detection and PVC classification.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.97-103
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• 2006

A modified one-sided measurement technique is proposed for Rayleigh wave (R-wave) velocity measurement in concrete. The scattering from heterogeneity may affect the waveforms of R-waves in concrete, which may make the R-waves dispersive. Conventional one-sided techniques do not consider the scattering dispersion of R-waves in concrete. In this study, the maximum energy arrival concept is adopted to determine the wave velocity by employing its continuous wavelet transform. Experimental study was performed to show the effectiveness of the proposed method. The present method is applied to monitor the strength development of early-age concrete. A series of experiments were performed on early-age concrete specimens with various curing conditions. Results reveal that the proposed method can be effectively used to measure the R-wave velocity in concrete structures and to monitor the strength development of early-age concrete.