• Journal of Sensor Science and Technology
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• v.12 no.3
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• pp.121-127
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• 2003

In this study, we carried out a study for implementation of the pre-amplifier and the digital signal processing part for the potable EEG biofeedback system. As we consider characteristics of the EEG signal, we designed the pre-amplifier to obtain the EEG signal to be reduced noise signal. Because the EEG signal include EOG, EMG, ECG signals etc, it is difficult to analyze of the EEG signal. Therefore, we developed DSP board and operation program which was embed the LMS adaptive filter algorithm and operate with the pre-amplifier in the real time. The simulation signal and pure EEG signal is used in the experiment. As the result, we confirmed good efficiency of developed system and possibility of application to the portable EEG biofeedback system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1047-1054
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• 2012

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and its risk increases with age. Conventionally, the way of detecting AF was the time·frequency domain analysis of RR variability. However, the detection of ECG signal is difficult because of the low amplitude of the P wave and the corruption by the noise. Also, the time·frequency domain analysis of RR variability has disadvantage to get the details of irregular RR interval rhythm. In this study, we describe an atrial fibrillation pattern analysis based on symbolization and information entropy. We transformed RR interval data into symbolic sequence through differential partition, analyzed RR interval pattern, quantified the complexity through Shannon entropy and detected atrial fibrillation. The detection algorithm was tested using the threshold between 10ms and 100ms on two databases, namely the MIT-BIH Atrial Fibrillation Database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.450-460
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• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• Journal of Veterinary Clinics
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• v.24 no.2
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• pp.233-237
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• 2007

A 10-month-old intact male Labrador Retriever dog was referred with the primary complaint of exercise intolerance, especially after vigorous exercise. Physical examination revealed split S1 and grade III/VI diastolic regurgitant murmur at the left apex and base, respectively. ECG finding was normal sinus rhythm at rest, but supraventricular tachycardia with bundle branch blocks after exercise. Thoracic radiography revealed dilated ascending aorta with normal range of cardiac silhouette (VHS 10.2). Echocardiography revealed abnormal valvular structures just above the aortic valvular cusps causing aortic regurgitation with a reduction of left ventricular ejection fraction (LVEF). Based on those findings, the case was diagnosed as congenital aortic regurgitation caused by abnormal valvular structures. The dog was managed with diltiazem and exercise restriction. This is a rare case of aortic deformity in dogs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.825-832
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• 2014

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. Even if some methods have the advantage in low complexity, but they generally suffer form low sensitivity. Also, it is difficult to detect PVC accurately because of the various QRS pattern by person's individual difference. Therefore it is necessary to design an efficient algorithm that classifies PVC based on QRS pattern in realtime and decreases computational cost by extracting minimal feature. In this paper, we propose PVC classification based on QRS pattern using QS interval and R wave amplitude. For this purpose, we detected R wave, RR interval, QRS pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of R wave detection, PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 PVC. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 93.72% in PVC classification.

• Progress in Medical Physics
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• v.13 no.4
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• pp.224-228
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• 2002

Myocardial tagging technique such as spatial modulation of magnetization (SPAMM) allows the study of myocardial motion with high accuracy. However, the accuracy of the estimation of tag intersection can be affected by tagline spacing. The aim of this study was to investigate the relationship between tagline spacing of SPAMM image and tagging contrast-to-noise ratio (CNR) in in-vivo study. Two healthy volunteers were undergone electrocardiographically triggered MR imaging with SPAMM-based tagging pulse sequence at a 1.5T MR scanner. Horizontally modulated stripe patterns were imposed with a range from 3.6 to 9.6 mm of tagline spacing. Images of the left ventricle(LV) wall were acquired at the mid-ventricle level during cardiac cycle with FE-EPI (TR/TE = 5.8/2.2 msec, FA= 10$^{\circ}$. Tagging CNR for each image was calculated with a software which developed in our group. During contraction, tagging CNR was more rapidly decreased in case of narrow tagline spacing than in case of wide tagline spacing. In the same heart phase, CNR was increased corresponding with tagline spacing. Especially, at the fully contracted heart phase, CNR was more rapidly increased than the other heart phases as a function of tagline spacing. The results indicated that the optimization of tagline spacing provides better tagging CNR in order to analyze the myocardial motion more accurately.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1947-1954
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• 2013

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require accurate detection of P-QRS-T point, higher computational cost and larger processing time. But it is difficult to detect the P and T wave signal because of person's individual difference. 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 arrhythmia detection based on binary coding using QRS feature varibility. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.