• The Korean Journal of Pharmacology
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• v.11 no.2
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• pp.9-17
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• 1975

The adrenergic blocking activity and refractory period of cardiac muscle on isolated rabbit atria were measured after administration of Scutellaria. In rabbits and cats the antiarrhythmic action of Scutellaria on atrial and ventricular arrhythmias produced by epinephrine or ouabain was examined and also compared with that of propranolol and quinidine. The alcoholic extract of Scutellaria produced a marked decrease in heart rate and contractile amplitude of the isolated rabbit atria. Pretreatment with Scutellaria rendered the atria to fail to respond to epinephrine, indicating that this crude drug possesses an adrenergic blocking activity. The extract produced a marked prolongation of the refractory period of atrial muscle. The extract effectively abolished the spontaneous arrhythmia occurring in the isolated rabbit atria. As propranolol and quinidine it also suppressed the atrial arrhythmia induced by ouabain. The extract prevented, as propranolol and quinidine, the induction of ventricular arrhythmia arising from excessive dose of epinephrine in anesthetized rabbits and cats. With regard to the ventricular arrhythmia induced by a continuous infusion of ouabain, the alcoholic extract of Scutellaria exerted some suppressive effect in anesthetized rabbits but no effect on cats. From the above results, it may be concluded that Scutellaria is effective against atrial and ventricular arrhythmias. The antiarrhythmic effects of this drug may be the result of adrenergic beta receptor blocking and cardiac depressive activities including prolongation of the refractory period of cardiac muscle.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.310-314
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• 2023

In this paper, we show that the limitations of Holter monitoring and Wearable Electrocardiogarphy Devices and their arrhythmia detection. Sudden death caused by cardiovascular disease, often referred to as the "silent killer" due to its unpredictable nature, is a major health concern. Electrocardiography (ECG) is a basic diagnostic tool for detecting heart disease, but its limitations make it difficult to detect arrhythmia, a significant indicator of an irregular heart state. To address this limitation, a long-term continuous ECG recording device has been developed, Holter ECG device and wearable device. A significant number of studies have focused on the differences between Holter monitoring and wearable devices. The Holter tests were useful for detecting regularly occurring arrhythmias, whereas wearable patches were better at detecting random and infrequent arrhythmias. Wearable patches were effective in detecting episodes of arrhythmia and myocardial ischemia. Despite the concern, wearable devices had less signal loss than Holter monitoring and patients also preferred wearable devices over Holter monitoring due to convenience. These results could mean that the wearable devices can perfectly replace the Holter test.

• Investigative Magnetic Resonance Imaging
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• v.21 no.1
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• pp.20-27
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• 2017

Purpose: To evaluate quantification results of single breath-hold (SBH) magnetic resonance (MR) cine imaging compared to results of conventional multiple breath-hold (MBH) technique for left ventricular (LV) function in patients with cardiac arrhythmia. Materials and Methods: MR images of patients with arrhythmia who underwent MBH and SBH cine imaging at the same time on a 1.5T MR scanner were retrospectively reviewed. Both SBH and MBH cine imaging were performed with balanced steady state free precession. SBH scans were acquired using temporal parallel acquisition technique (TPAT). Fifty patients ($65.4{\pm}12.3years$, 72% men) were included. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), myocardial mass, and LV regional wall motion were evaluated. Results: EF, myocardial mass, and regional wall motion were not significantly different between SBH and MBH acquisition techniques (all P-values > 0.05). EDV, ESV, and SV were significant difference between the two techniques. These parameters for SBH cine imaging with TPAT tended to lower than those in MBH. EF and myocardial mass of SBH cine imaging with TPAT showed good correlation with values of MBH cine imaging in Passing-Bablok regression charts and Bland-Altman plots. However, SBH imaging required significantly shorter acquisition time than MBH cine imaging ($15{\pm}7sec$ vs. $293{\pm}104sec$, P < 0.001). Conclusion: SBH cine imaging with TPAT permits shorter acquisition time with assessment results of global and regional LV function comparable to those with MBH cine imaging in patients with arrhythmia.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4443-4449
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• 2011

ECG consists of various types of electrical signal on the heart, and feature point of these signals can be detected by analyzing the arrhythmia. So far, feature points extraction method for the detection of arrhythmia done in the many studies. However, it is not suitable for portable device using real time operation due to complicated operation. In this paper, R-peak were extracted using R-R interval and QRS width informations on patients. First, noise of low frequency bands eliminated using butterworth filter, and the R-peak was extracted by R-R interval moving average and QRS width moving average. In order to verify, it was experimented to compare the R-peak of data in MIT-BIH arrhythmia database and the R-peak of suggested algorithm. As a results, it showed an excellent detection for feature point of R-peak, even during the process of operation could be efficient way to confirm.

• Journal of Biomedical Engineering Research
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• v.41 no.5
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• pp.185-194
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• 2020

Arrhythmia is basically diagnosed with the electrocardiogram (ECG) signal, however, ECG is difficult to measure and it requires expert help in analyzing the signal. On the other hand, the radial pulse can be measured with easy and uncomplicated way in daily life, and could be suitable bio-signal for the recent untact paradigm and extensible signal for diagnosis of Korean medicine based on pulse pattern. In this study, we developed an irregular radial pulse detection algorithm based on a learning model and considered its applicability as arrhythmia screening. A total of 1432 pulse waves including irregular pulse data were used in the experiment. Three data sets were prepared with minimal preprocessing to avoid the heuristic feature extraction. As classification algorithms, elastic net logistic regression, random forest, and extreme gradient boosting were applied to each data set and the irregular pulse detection performances were estimated using area under the receiver operating characteristic curve based on a 10-fold cross-validation. The extreme gradient boosting method showed the superior performance than others and found that the classification accuracy reached 99.7%. The results confirmed that the proposed algorithm could be used for arrhythmia screening. To make a fusion technology integrating western and Korean medicine, arrhythmia subtype classification from the perspective of Korean medicine will be needed for future research.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.464-469
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• 2015

Long-term electrocardiogram data can be acquired by linking a Holter monitor to a mobile phone. However, most systems are designed to detect arrhythmia through heartbeat classification, and not just for supporting clinical decisions. In this paper, we propose an Abstracting algorithm, and introduce an analogous pateint search system using this algorithm. An analogous patient searcher summarizes each patient's typical pattern using the results of heartbeat, which can greatly simplify clinical activity. It helps to find patients with similar arrhythmia patterns, which can help in contributing to diagnostic clues. We have simulated these processes on data from the MIT-BIH arrhythmia database. As a result, the Abstracting algorithm provided a typical pattern to assist in reaching rapid clinical decisions for 64% of the patients. On an average, typical patterns and results generated by the abstracting algorithm summarized the results of heartbeat classification by 98.01%.

• 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 Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.62-69
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• 2022

An electrocardiogram (ECG) is one of the most important biosignals, and in particular, continuous ECG monitoring is very important in patients with arrhythmia. There are many different types of arrhythmia (sinus node, sinus tachycardia, atrial premature beat (APB), and ventricular fibrillation) depending on the cause, and continuous ECG monitoring during daily life is very important for early diagnosis of arrhythmias and setting treatment directions. The ECG signal of arrhythmia patients is very unstable, and it is difficult to detect the R-peak point, which is a key feature for automatic arrhythmias detection. In this study, we develped a continuous measuring Holter ECG monitoring device and software for analysis and confirmed the utility of R-peak of the ECG signal with MIT-BIH arrhythmia database. In future studies, it needs the validation of algorithms and clinical data for morphological classification and prediction of arrhythmias due to various etiologies.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.204-210
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• 2011

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

• Journal of the Korean Mathematical Society
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• v.43 no.4
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• pp.885-897
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• 2006

A new mathematical model of pumping heart coupled to lumped compartments of blood circulation is presented. This lumped pulsatile cardiovascular model consists of eight compartments of the body that include pumping heart, the systemic circulation, and the pulmonary circulation. The governing equations for the pressure and volume in each vascular compartment are derived from the following equations: Ohm's law, conservation of volume, and the definition of compliances. The pumping heart is modeled by the time-dependent linear curves of compliances in the heart. We show that the numerical results in normal case are in agreement with corresponding data found in the literature. We extend the developed lumped model of circulation in normal case into a specific model for arrhythmia. These models provide valuable tools in examining and understanding cardiovascular diseases.