• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.233-242
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• 2018

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 higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2003.05a
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• pp.148-153
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• 2003

본 논문에서는 심근허혈 질환을 효율적으로 분류하기 위한 신경망을 설계하였다. European ST-T DB의 심전도로부터 ST 분절의 특징을 추출하여 입력노드를 결정하고 10개의 학습률과 학습 횟수에 따른 신경망의 MES를 계산하였다. 실험 결과 특징 파라미터의 조합을 ST0, ST80, Slope, Area로 하였을 때 MSE를 가장 작았다. 이러한 특징 파라미터를 이용하여 신경망의 입력으로 학습시킨 경우 학습 횟수의 증가에 따라 MSE가 지수합수적으로 감소하였으며 1,000회 이상에서는 둔하게 감소하였다. 또한 학습 횟수가 5,000회, 10,000회, 15,000회 각각의 경우에 대하여 학습률을 0.01부터 0.7까지 증가시키면서 MSE를 계산한 결과 학습 횟수가 증가할수록 MSE를 최소로 하는 최적학습률이 0.1부터 0.04까지 감소하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.839-842
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• 2007

현재 센서 네트워크기반에 다양한 적용으로 인하여 데이터 통신에 량이 많아지고 있다. 기존에 환경 모니터링 등과 같이 조도, 온도, 습도를 다루는 것에서 ECG, EKG, GPS등과 같은 비교적 센서 네트워크 환경에서 대용량 데이터를 다루어지고 있다. 이러한 점은 희박한 자원을 바탕하는 USN환경에서 문제가 된다. 이 문제는 기존 연구 방향에서 데이터를 더 적게 전송하여 더 많은 정보를 주는 것에 포커스가 되었다. 하지만 이는 근본적으로 해결될 수 없다. 본 제안된 알고리즘은 데이터를 효율적으로 압축함으로서 이를 해결하였다.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.393-395
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• 2009

심장의 활성 근육의 움직임에 의하여 발생되는 전기적 변화량을 나타내는 심전도는 부정맥 또는 허혈성 심장질환을 진단하는데 널리 활용되고 있다. 특히 심실빈맥(Ventricular Tachycardia) 또는 심실세동(Ventricular Fibrillation)과 같이 치명적인 심장리듬이 발생하기 이전에, 심실조기수축(Ventricular Premature Contraction)을 검출하여 생명을 위협할 수 있는 부정맥을 조기에 진단할 수 있는 연구들이 일부 진행되고 있다. 이에 따라서 본 연구에서는 심전도 신호의 R-R 간격 정보와 R-peak 정보의 진위성을 판단하여 PVC 부정맥 패턴뿐만 아니라 PVC 파형이 연속적으로 진행되는 PVC-RUNs을 효율적으로 검출할 수 있는 부정맥 진단 알고리즘을 제안하고자 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.733-734
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• 2015

사용자 인증의 중요성이 강조되고 있는 추세에 따라 지문인식, 홍채인식, 안면 인식 등 생체인증 기술이 적용되어 디바이스 보안성이 향상되고 있다. 이런 흐름에 맞춰 심장박동패턴을 이용하는 새로운 보안 기술이 선보여지고 있는 가운데 심전도(ECG)방식을 통해 사용자인증을 하는 Nymi Band라는 웨어러블이 관심을 받고 있다. 그러나 해당 디바이스의 펌웨어 조작 가능성이 보안 취약점으로 제시되어 본 논문에서는 Nymi Band의 핵심 기술 분석을 거쳐 펌웨어 공격의 구체적인 시나리오를 구상하고, 그에 따른 대응 방안을 제안하는 연구를 진행하고자 한다.

• 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.

• 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.15 no.10
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• pp.2223-2230
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• 2011

ECG is used to diagnose heart diseases such as myocardial ischemia, arrhythmia and myocardial infarction. Particularly, myocardial ischemia causes the shape change of the ST segment, this change is transient and may occur without symptoms. So it is important to detect the transient change of ST segment through long term monitoring. ST segment classification algorithm for making diagnosis myocardial ischemia is presented in this paper. The first step in the ST segment shape classification process is to detect R wave point and feature points based adaptive threshold and window. And then, the suggested algorithm detects the ST level change, To classify the ST segment shape, the suggested algorithm uses the slope values of the four points between the S and T wave. The ECG data in the European ST-T database were used to verify the performance of the developed algorithm. The best correct rate was 99.40% and the worst correct rate was 68.48%.

• YAKHAK HOEJI
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• v.53 no.2
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• pp.51-59
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• 2009

Quantitative analysis of (+)-catechin (C), (-)-epigallocatechin (EGC), (-)-epicatechin (EC), (-)-epigallocatechin gallate (EGCG), (-)-epicatechin gallate (ECG) and caffeine in commercial green tea was carried out by HPLC employing gradient elution of 0.1% acetic acid and acetonitrile on ODS column. The optimized HPLC method provided satisfactory linearity, accuracy and precision. The relationship between the concentration of the components and cultivated region of the commercial green tea was not significant, while the concentration of EGCG, ECG and caffeine decreased significantly in the later harvested green tea samples (p<0.01). Multivariate analysis of the components was performed in order to characterize and evaluate the cultivated region and harvest period-related variation. Hierarchical clustering and discriminant analysis were applied to classify the geographical and seasonal origins of the green tea samples. The classification accuracy of the cultivated region and harvest period by discriminant analysis was 95% and 91%, respectively, indicating that this method could be reliable and convenient for the quality control of herbal products with different origin.