• 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.

• 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.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.13-16
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• 1990

This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm represents more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it expresses the complicated medical problems simply and clearly, those are not solved by the mathematical methods. The Decision-Tables have very simple structure and so it is very easy to correct or expand the system by adding or correcting some rules.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.171-178
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• 1990

This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm provides more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it can express clearly the complicated medical problems those are not solved by the mathematical methods. The Decision-Tables have very simple structure. Therefore, it is very easy to correct or expand the system by adding or correcting some rules.

• 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.

• Clinical and Experimental Pediatrics
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• v.60 no.11
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• pp.344-352
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• 2017

Arrhythmias in the neonatal period are not uncommon, and may occur in neonates with a normal heart or in those with structural heart disease. Neonatal arrhythmias are classified as either benign or nonbenign. Benign arrhythmias include sinus arrhythmia, premature atrial contraction, premature ventricular contraction, and junctional rhythm; these arrhythmias have no clinical significance and do not need therapy. Supraventricular tachycardia, ventricular tachycardia, atrioventricular conduction abnormalities, and genetic arrhythmia such as congenital long-QT syndrome are classified as nonbenign arrhythmias. Although most neonatal arrhythmias are asymptomatic and rarely life-threatening, the prognosis depends on the early recognition and proper management of the condition in some serious cases. Precise diagnosis with risk stratification of patients with nonbenign neonatal arrhythmia is needed to reduce morbidity and mortality. In this article, I review the current understanding of the common clinical presentation, etiology, natural history, and management of neonatal arrhythmias in the absence of an underlying congenital heart disease.

• Clinical and Experimental Pediatrics
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• v.53 no.6
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• pp.680-687
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• 2010

Patients with congenital heart diseases (CHD) are confronted with early- and late-onset complications, such as conduction disorders, arrhythmias, myocardial dysfunction, altered coronary flow, and ischemia, throughout their lifetime despite successful hemodynamic and/or anatomical correction. Rhythm disturbance is a well-known and increasingly frequent cause of morbidity and mortality in patients with CHD. Predisposing factors to rhythm disturbances include underlying cardiac defects, hemodynamic changes as part of the natural history, surgical repair and related scarring, and residual hemodynamic abnormalities. Acquired factors such as aging, hypertension, diabetes, obesity, and others may also contribute to arrhythmogenesis in CHD. The first step in evaluating arrhythmias in CHD is to understand the complex anatomy and to find predisposing factors and hemodynamic abnormalities. A practical stepwise approach can lead to diagnosis and prompt appropriate interventions. Electrophysiological assessment and management should be done with integrated care of the underlying heart defects and hemodynamic abnormalities. Catheter ablation and arrhythmia surgery have been increasingly applied, showing increasing success rates with technological advancement despite complicated arrhythmia circuits in complex anatomy and the difficulty of access. Correction of residual hemodynamic abnormalities may be critical in the treatment of arrhythmia in patients with CHD.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.131-139
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• 2013

In this paper, we look into previous research in relation to each processing step for ECG diagnosis and propose detection and classification method of arrhythmia using rhythm features of ECG signal. Rhythm features for distribution of rhythm and heartbeat such as identity, regularity, etc. are extracted in feature extraction, and rhythm type is classified using rule-base constructed in advance for features of rhythm section in rhythm classification. Experimental results for all of rhythm types in the MIT-BIH arrhythmia database show detection performance of 100% for arrhythmia with only normal rhythm rule and applicability of classification for rhythm types with arrhythmia rhythm rules.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1217-1226
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• 1989

This paper describes a design of hardware system for real time automatic diagnosis of ECG arrhythmia based on pipeline processor consisting of three microcomputer. ECG data is acquisited by 12 bit A/D converter with hardware QRS triggered detector. Four diagnostic parameters-heart rate, morpholigy, axis, and ST segment-are used for the classification and the diagnosis of arrhythmia. The functions of the main CPU were distributed and processed with three microcomputers. Therefore the effective data process and the real time process using microcomputer can be obtained. The interconnection structure consisting of two common memory unit is designed to decrease the delay time caused by data transfer between processors and be which the delay time can be taken 1% of one clock period.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.294-305
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• 2009

The bio signals essentially have different characteristics in each person. And the main purpose of automatic diagnosis algorithm based on bio signals focuses on discriminating differences of abnormal state from personal differences. In this paper, we propose automatic ECG diagnosis algorithm which discriminates normal heart beats from premature ventricular contraction using optimization of wavelet parameterization to solve that problem. The proposed algorithm optimizes wavelet parameter to let energy of signal be concentrated on specific scale band. We can reduce the personal differences and consequently highlight the differences coming from arrhythmia via this process. The proposed algorithm using ELM as a classifier show high discrimination performance between normal beat and PVC. From the experimental results on MIT-BIH arrhythmia database the performances of the proposed algorithm are 98.1% in accuracy, 93.0% in sensitivity, 96.4% in positive predictivity, and 0.8% in false positive rate. This results are similar or higher then results of existing researches in spite of small human intervention.