• The Journal of the Korean dental association
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• v.40 no.9 s.400
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• pp.703-708
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• 2002

Cardiac arrhythmia is irregular heart rate. It's one of the reason of unpredictable sudden death. Accurate diagnosis and management of cardiac arrhythmia are the most important factors for the life of patient. To obtain a good prognosis, Dentist should be know and manage the multi-types of cardiac arrhythmia during dental treatment with the cooperation of medical doctor majored in cardiac circulation medicine. We casually found the cardiac arrhythmia in mandible prognathism patient during preparation for orthognathic surgery. Orthognathic surgery for cardiac arrhythmia patient was done successfully under general anesthesia with the temporary cardiac pace-maker.

• Journal of Chest Surgery
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• v.47 no.4
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• pp.416-419
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• 2014

Recurrent ventricular arrhythmia can be fatal and cause serious complications, particularly when it is caused immediately after an operation. Incorrect placement of a Swan-Ganz catheter can trigger life-threatening ventricular arrhythmia, but even intensive care specialists tend to miss this fact. Here, we report a case of recurrent ventricular arrhythmia causing a severe hemodynamic compromise; the arrhythmia was induced by a severely angulated Swan-Ganz catheter. The recurrent ventricular arrhythmia was not controlled by any measures including repositioning of the catheter, until the complete removal of the Swan-Ganz catheter. It is necessary to keep in mind that the position of the pulmonary artery catheter should be promptly checked if there is intractable recurrent ventricular arrhythmia.

• Journal of Veterinary Clinics
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• v.6 no.2
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• pp.281-290
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• 1989

To gain insight into the relationship between the occurrence of occlusive arrhythmia(OA) and the incidence of reperfusion arrhythmia(RA), this study used 25 open-chest dogs anesthetized with halothan, these were ligated between anterior ventricular branch and marginal branch of left circumflex artery for 30 minutes and occlusive arrhythmia were observed during the ligation. After releasing of the ligation, TA were observed during 5 minutes. The results were summerized as follow； 1. Such arrhythmias as ventricular fibrillation(VF), short run type VPC Premature contraction(VPC), Venticular tachycardia(VT), ventricularc and trigeminy VPC(TVPC) were observed during occlusion and reperfusion. 2. The cases occurred VT, SRVPC and TVPC during occlusion necessarily were Incidence of RA. 3. RA never occurred without appearence of occlusive arrhythmias. 4. The occurrence rate of OA showed 55.5% in the incidence group of RA and 24.6％ in the non incidence group of RA. 5. The occurrence rate of VPC during occlusion showed 9.9＋5.85(episode/min) in the incidence group of RA and 4.46＋5.88(episode/min) in the non-incidence group of RA. These results may be estimated that the occurrence of VT, SRVPC and TVPC, and the high occurrence rate of VPC during occlusion can be predicted the incidence.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.107-116
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• 2009

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.

• 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.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1665-1668
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• 2008

Arrhythmia causes sudden cardiac death. In the past, there were medical limitations in finding the cause of arrhythmia. As an alternative solution for research of arrhythmia, there have been studies to find the causes of arrhythmia by producing a virtual heart model. Medically, arrhythmia has two main causes: abnormal occurrence of action potential and abnormal conduction of action potential. Based on these, the tachycardia, which is one of the arrhythmia, was manifested and the phenomenon of ventricular fibrillation was numerically analyzed in this study. For this purpose, an electrophysiological model of ventricular cells was implemented, which was subsequently applied to the reaction-diffusion partial differential equation to interpret the macroscopic conduction phenomenon in two-dimensional tissues. The ventricular fibrillation refers to a condition where several irregular waves occur in cardiac tissue, whose generation mechanism is pathologically related to the cardiac tissue.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.85-89
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• 2014

Electrocardiogram (ECG) is a diagnostic test which records the electrical activity of the heart, shows abnormal rhythms and detects heart muscle damages. With this ECG signal, medical centers diagnose patients' heart disease symptoms. A normal resting heart rate for adults rages from 60 to 100 beats a minute. An irregular heartbeat is called "arrhythmia", and arrhythmia is also called "cardiac dysrhythmia". In an arrhythmia, the heartbeat maybe too slow(slower than 60beats), too rapid(faster than 100beats), too irregular, etc. Among these symptoms of arrhythmia, if the heart beat is slower than the normal range, the symptom is called "bradycardia", and if it is faster than the range, it is called "tachycardia" In this letters, we proposed the detection algorithm of cardiac arrhythmia in ECG signal using R-R interval through the detection of R-peak.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.33-40
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• 2016

Previous works for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods require accurate detection of ECG signal, higher computational cost and larger processing time. But it is difficult to analyze the ECG signal because of various noise types. Also in the healthcare system based IOT that must continuously monitor people's situation, it is necessary to process ECG signal in realtime. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose R wave detection considering complexity and arrhythmia classification based on binary coding. For this purpose, we detected R wave through SOM and then RR interval from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. R wave detection and PVC, PAC, Normal classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.41%, 97.18%, 94.14%, 99.83% in R wave, PVC, PAC, Normal.

• 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 Biomedical Engineering Research
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• v.43 no.6
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• pp.399-408
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• 2022

Because arrhythmia occurs irregularly, it should be examined for at least 24 hours for accurate diagnosis. For this reason, this paper developed firmware software for arrhythmia detection and prevented consumption of temporal and human resources and enabled continuous management and early diagnosis. Prior to the experiment, the interval between the R peaks of the QRS Complex was calculated using the Pan-Tompkins algorithm. The developed firmware software designed and implemented an algorithm to detect arrhythmia such as tachycardia, bradycardia, ventricular tachycardia, persistent tachycardia, and non-persistent tachycardia, and a data transmission format to monitor the collected data based on BLE. As a result of the experiment, arrhythmia was found in real time according to the change in BPM as designed in this paper. And the data quality for BLE communication was verified by comparing the sensor's serial communication value with the Android application reception value. In the future, wearable devices for real-time arrhythmia detection will be lightweight and developed firmware software will be applied.