• Journal of IKEEE
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• v.13 no.1
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• pp.77-86
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• 2009

A wavelet Electrocardiogram(ECG) detector and its analog-to-digital converter(ADC) for low-power implantable cardiac pacemakers are presented in this paper. The proposed wavelet-based ECG detector consists of a wavelet decomposer with wavelet filter banks, a QRS complex detector of hypothesis testing with wavelet-demodulated ECG signals, and a noise detector with zero-crossing points. To achieve high-detection performance with low-power consumption, the multi-scaled product algorithm and soft-threshold algorithm are efficiently exploited. To further reduce the power dissipation, a low-power ADC, which is based on a Successive Approximation Register(SAR) architecture with an on/off-time controlled comparator and passive sample and hold, is also presented. Our algorithmic and architectural level approaches are implemented and fabricated in standard $0.35{\mu}m$ CMOS technology. The testchip shows a good detection accuracy of 99.32% and very low-power consumption of $19.02{\mu}W$ with 3-V supply voltage.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.818-825
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• 2016

The existing R-peak detection research suggests improving the distortion of the signal such as baseline variations in ECG signals by using preprocessing techniques such as a bandpass filtering. However, preprocessing can introduce another distortion, as it can generate a false detection in the R-wave detection. In this paper, we propose an R-peak detection algorithm in ECG signal, based on primitive signal in order to detect reliably an R-peak in baseline variation. First, the proposed algorithm decides the primitive signal to represent the QRS complex in ECG signal, and by scaling the time axis and voltage axis, extracts multiple primitive signals. Second, the algorithm detects the candidates of the R-peak using the value of the voltage. Third, the algorithm measures the similarity between multiple primitive signals and the R-peak candidates. Finally, the algorithm detects the R-peak using the mean and the standard deviation of similarity. Throughout the experiment, we confirmed that the algorithm detected reliably a QRS group similar to multiple primitive signals. Specifically, the algorithm can achieve an R-peak detection rate greater than an average rate of 99.9%, based on eight records of MIT-BIH ADB used in this experiment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.37-43
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• 2020

Recently, many researches have been actively to diagnose symptoms of heart disease using ECG signal, which is an electrical signal measuring heart status. In particular, the electrocardiogram signal can be used to monitor and diagnose arrhythmias that indicates an abnormal heart status. In this paper, we proposed 1-D convolutional neural network for arrhythmias classification systems. The proposed model consists of deep 11 layers which can learn to extract features and classify 5 types of arrhythmias. The simulation results over MIT-BIH arrhythmia database show that the learned neural network has more than 99% classification accuracy. It is analyzed that the more the number of convolutional kernels the network has, the more detailed characteristics of ECG signal resulted in better performance. Moreover, we implemented a practical application based on the proposed one to classify arrythmias in real-time.

• The Korean Journal of Physiology
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• v.19 no.1
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• pp.61-72
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• 1985

In order to compare the cardiac function of various groups of athletes, the resting electrocardiographic time intervals, amplitudes and vectors were analyzed in high school athletes of throwing(n=7), jumping(n=11), short track(n=8), long track(n=14), boxing(n=7), volleyball(n=8) and baseball(n=9), and nonathletic control students(n= 19). All athletic groups showed a significantly longer R-R interval(0.96-1.09 sec) than the controls (0.78 sec). Therefore, the heart rate was significantly slower in atheletes than in the control, but was not different among the different athletic groups. R-R interval is the sum of intervals of P-R, 0-T and T-P: P-R and Q-T intervals showed no difference among the control and athletic groups, but T-P interval in the jump, short track, long track and boxing groups was significantly higher than the control. R-B interval showed a significant correlation with T-P or Q-T intervals but no correlation with P-R or QRS complex. Comparing the amplitude of electrocardiographic waves, the athletic groups showed a lower trend in P wave than the controls. T wave in lead $V_5\;(Tv_5)$ was similar in the athletic and control groups. The long track group showed a significantly higher waves of $Rv_5$, $Sv_1$, and the sum of $Rv_5$ and $Sv_1$ than not only the controls but also the other athletic group. The angles of P, QRS, and T vector in the frontal and horizontal planes were not different among the control and all the athletic groups. Each athletic group stowed a lower trend in amplitude of P vector in the frontal plane, but in horizontal plane, throwing, jump, short track and baseball groups showed a significantly lower than the controls. The amplitude of QRS and T vector was similar in the athletic and control groups, but only the baseball group showed a significantly higher QRS vector in the frontal plane. In taken together, all the athletic groups showed a slower heart rate than the controls, mainly because of elongated T-P interval. Comparing the electrocardiographic waves and vector, the athletic groups showed lower amplitudes of P wave and P vector than the controls. Values of $Rv_5$ and $Sv_1$ strongly suggest that only the long distance runners among the various athletic groups developed a left ventricular hypertrophy.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.

• Journal of Veterinary Clinics
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• v.8 no.1
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• pp.93-101
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• 1991

Experimental obstruction of urethra was induced for 66 hours on 5 male dogs to determine the diagnostic value of ECG and blood chemical values. The results obtained in the examination were summarized as follows ; 1. Changes of ECG, blood chemical values and clinical sings were observed from 30 hours after urethral obstruction. 2. In ECG, the peaked and amplitude of T wave were increased remarkably. p wave were decreased, and intervals of PR(Q) and QRS complex were prolonged after urethral obstruction. 3. The levels of BUN, potassium, creatinine in serum after urethral obstruction were increased remarkably and correlation between these levels and the increase of amplitude in T wave were recognized to be significant.

• Journal of Dental Anesthesia and Pain Medicine
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• v.20 no.2
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• pp.89-93
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• 2020

We experienced an unusual case of accelerated junctional rhythm with severe hypotension after infiltration of lidocaine containing epinephrine during dental surgery under general anesthesia. The patient's electrocardiogram exhibited retrograde P-waves following the QRS complex, which could be misinterpreted as ST-segment depression. As a temporary measure, administration of ephedrine restored the patient's blood pressure to normal levels. The importance of this case lies in its demonstration of an unexpected and serious side effect of commonly used epinephrine infiltration. This case also highlights the need for accurate interpretation of the electrocardiogram and comprehensive understanding of best practices for patient management.

• Journal of Veterinary Clinics
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• v.16 no.2
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• pp.239-247
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• 1999

We estimated the changes of ECG and electrolytes in serum after intravenous administration of Succinylcholine Chloride (SCC), 0.15 mg/kg in ten normal mongrel dogs (mean 13 kg). Hyperkalemia was observed in the highest level by 6.46$\pm$0.8 mEq/L at ten minutes after the administration of SCC. The ECG appeared temporary the most severe changes as the increased T wave, the disappeared P wave, the prolonged of conduction times (PR, QTc intervals and QRS complex), and arrhythmia as ventricular premature contraction at 3 and 5 minutes after the administration SCC. Therefore, the changes of ECG after administration of SCC were suggested to specific and independent from hyperkalemic changes. Because these changes were observed to differ from ECG by hyperkalemia, and the highest period of $K^＋$value in serum differ from the appearance period of severe changes of ECG and arrhythmia by SCC.

• Korean Journal of Veterinary Research
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• v.19 no.1
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• pp.69-73
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• 1979

In order to detect the influence of overdosed combelen on the Korean native goats, 5mg of combelen per kg of body weight were injected intramusculary. Clinical findings, electrocardiograms and some blood chemical values were determined and the results obtained were as follows: 1. After administration of 5 mg/kg dose of combelen the animals were moderately depressed and often tried to take sitting position but showed moderate response to pinching stimulation of skin. 2. Body temperature and respiratory rate were not suppressed but heart rate were accelerated during sedative stage. 3. During sedation a slight change in the QRS complex and T wave appeared but it was not significant. 4. Hemoglobin concentration showed a tendency to decrease. SGOT activities were markedly increased 24 hours after administration of combelen and BUN values were slightly lowered. Blood sugar values were markedly elevated during sedative stage.