• Journal of Biomedical Engineering Research
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• v.12 no.1
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• pp.63-70
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• 1991

Design of Arrhythmia Automatic Diagnostic System Using Decision Table We have developed an arrhythmia automatic diagnostic system using decision table which is based on the criteria of Minnesota code. This system is divided into two Parts. One is wave detection algorithm using significant point extraction method, the other is arrhythmia diag- nostic algorthm. The proposed system allows physicians to diagnose more accurately by pro- viding the objective information about a lot of computer -processed ECG data.

• 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.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.746-749
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• 1988

This paper describes an arrhythmia detection algorithm using PP and PR Interval. In order to detect P-wave accurately, an improved 5-point derivative method is used. In this paper, the RR, PP and PR interval. and the number of P-waves per RR Interval are detected for arrhythmia detection. These parameters can be utilized to diagnose in the varied types of AV block, atrial fibrillation, and PVC.

• 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.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.29-31
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• 2005

Detection methods for atrial tachycardia and fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.142-145
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• 2006

Detection methods for atrial tachycardia and atrial fibrillation on the time axis have the advantages of light operational load and are easy to apply to various applications. Despite these advantages, arrhythmia detection algorithm on the time axis cannot stand much noise such as motion artifacts, moreover the peak detection algorithm has high complexity. In this paper, we use a spectrum analysis method for the detection of atrial tachycardia and atrial fibrillation. By applying spectrum analysis and digital filtering on obtained electrogram signals, we can diagnose heart arrhythmia without using peak detection algorithm.

• 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.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.292-297
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• 2013

In this paper, unusual waveform detection algorithm based on Refractory Period in arrhythmia ECG signal is proposed. Most of arrhythmia ECG signals consist of unusual waveforms with average 10% rate. Thus tremendous benefit can be obtained in terms of time and cost by providing unusual waveform samples reduced more than 90% to medical staffs who have to monitor and analyze for a long time. The proposed algorithm detects the R-peak using the features of R wave and variable refractory period. For the detected R-peak, unusual waveforms are found using means and standard deviation of electric potential and kurtosis of the R-peaks which are not included in unusual waveform. The proposed algorithm was applied to all records of the MIT-BIH arrhythmia database and showed more than average 90% of compression ratio.

• 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.