• 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 the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1279-1286
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• 2021

The electrocardiogram (ECG) signal is a good indicator for early diagnosis of heart abnormalities. The ECG signal has a different reference normal signal for each person. And it requires lots of data to diagnosis. In this paper, we propose an adaptive OpenCL-based FPGA-GPU hybrid-layer platform to efficiently accelerate ECG signal diagnosis. As a result of diagnosing 19870 number of ECG signals of MIT-BIH arrhythmia database on the platform, the FPGA accelerator takes 1.15s, that the execution time was reduced by 89.94% and the power consumption was reduced by 84.0% compared to the software execution. The GPU accelerator takes 1.87s, that the execution time was reduced by 83.56% and the power consumption was reduced by 62.3% compared to the software execution. Although the proposed FPGA-GPU hybrid platform has a slower diagnostic speed than the FPGA accelerator, it can operate a flexible algorithm according to the situation by using the GPU.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.69-78
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• 1992

This Paper describes implementation of a remote ECG diagnostic system using telephone line. The overall system includes ECG data acquisition system, ECG terminal, system control software, automatic diagnosis system, and transmission system.'The proposed system provides various functions, which are ECG data acquisition, transmission, receiving, diagnosis and dialogue between patients and medical doctors. Thls system is very simple and convienient to use. We evaluate the performance of modem and the accuracy of automatic diagnosis algorithm. The obtained results suggest the Possibilities of a remote ECG diagnostic system using the only existed telephone line.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.427-436
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• 1999

This paper describes an ECG signal labeling algorithm based on fuzzy clustering, which is very useful to the automated ECG diagnosis. The existing labeling methods compares the crosscorrelations of each wave form using IF-THEN binary logic, which tends to recognize the same wave forms such as different things when the wave forms have a little morphological variation. To prevent this error, we have proposed as ECG signal labeling algorithm using fuzzy clustering. The center and the membership function of a cluster is calculated by a cluster validity function. The dominant cluster type is determined by RR interval, and the representative beat of each cluster is determined by MF (Membership Function). The problem of IF-THEN binary logic is solved by FCM (Fuzzy C-Means). The MF and the result of FCM can be effectively used in the automated fuzzy inference -ECG diagnosis.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.191-198
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• 2020

In this study, we proposed a real-time ECG analytical method for predicting stress and dangerous heart condition using the ECG signal in playing AR/VR device. A real-time diagnosis is used as R-R interval based HRV(:Heart rate variability), BPM(:Beats Per Minitue) and autonomic nervous research with through mapping method of two-dimensional planes. The ECG data were analyzed every 5 minutes and derived from autonomic nervous system diagnosis.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• Journal of Biomedical Engineering Research
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• v.3 no.2
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• pp.83-94
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• 1982

This paper presents a practical approach to develop computer algorithm for automatic diagnosis electrocardiogram(ECG) based on a decision table techniques.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.495-501
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• 2006

Diagnostics of unborn baby is mainly aimed at prediction and detection of occurrence of intrauterine hypoxia. Consequences resulting from fetal hypoxia appear in its heart activity. In this study, we have developed a new non-invasive system for fetal hypoxia diagnosis which provides systolic time interval(STI) parameters on the basis of analysis of electrical and mechanical heart activity together. For this we have worked on 1) the proper lead system for the acquisition of abdominal ECG, 2) the independent component analysis based signal processing and fetal ECG separation, 3) the development of a hardware which consists of an abdominal ECG amplifying module and an ultrasound module and 4) the detection of characteristic points of FECG and Doppler signal and the extraction of diagnostic parameters. The developed system was evaluated by the clinical experiments in which 33 subjects were participated. The acquired STI by the system were distributed within the ranges from the well-established invasive results of other researchers. From this, we can conclude that the developed non-invasive fetal hypoxia diagnosis system is useful.

• The Journal of the Korean life insurance medical association
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• v.26
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• pp.21-30
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• 2007

ECG is included in certain medical examinations of insurance application, ECG has low specificity and sensitivity. So ECG is not usually used to diagnose specific diseases. But, ECG is not invasive and costs low. So ECG is usually used in underwriting. Actually in underwriting we meet various ECG patterns and diagnosises. Understanding of various ECG patterns is different between insurance medicine and clinical medicine. So We have to learn various ECG patterns and effects on mortality and morbidity. First considerations of ECG readings are age, sex, blood pressure, family history, smoking historyalcohol history and hyperlipidemia. These are predictors for possibility of disease. Also it is important to review recording ECG with proper skill. In this review I consider several ECG diagnosises that we meet frequently, which is, LVH, RVH, ST abnormalities, LBBS, RBBB, A-B blocks, several kinds of arrhythmia. We have to consider long term mortalities and morbidities of specific ECG patterns although applicants have no symptom and sign. And then we have to make underwriting manual according to specific ECG diagnosises and patterns and underwrite precisely ECG patterns according to insurance products. Nowadays coronary heart disease and other heart diseases are increasing in Korea. So we have to learn various ECG patterns and research mortalities and morbidities of abnormal ECG patterns. Also we have to apply to more broad, precise underwriting skills about ECG patterns and diagnosises.