• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2655-2663
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• 2011

Several algorithms have been developed to detect AF which rely either on the form of P waves or the based on the time frequency domain analysis of RR variability. However, locating the P wave fiducial point is very difficult because of the low amplitude of the P wave and the corruption by 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 detection algorithm through non-linear analysis of irregular RR interval rhythm based on the variability, randomness and complexity. We employ a new statistical techniques root mean squares of successive differences(RMSSD), turning points ratio(TPR) and sample entropy(SpEn). The detection algorithm was tested using the optimal threshold on two databases, namely the MIT-BIH Atrial Fibrillation Database and the Arrhythmia Database. We have achieved a high sensitivity(Se:94.5%), specificity(Sp:96.2%) and Se(89.8%), Sp(89.62%) respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2551-2561
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• 2014

Several algorithms have been developed to detect AFIB(Atrial Fibrillation) which either rely on the linear and frequency analysis. But they are more complex than time time domain algorithm and difficult to get the consistent rule of irregular RR interval rhythm. In this study, we propose algorithm for optimal value detection of irregular RR interval for AFIB classification based on linear analysis. For this purpose, we detected R wave, RR interval, from noise-free ECG signal through the preprocessing process and subtractive operation method. Also, we set scope for segment length and detected optimal value and then classified AFIB in realtime through liniar analysis such as absolute deviation and absolute difference. The performance of proposed algorithm for AFIB classification is evaluated by using MIT-BIH arrhythmia and AFIB database. The optimal value indicate ${\alpha}=0.75$, ${\beta}=1.4$, ${\gamma}=300ms$ in AFIB classification.

• 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.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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.11a
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• pp.117-121
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• 2001

본 연구에서는 향에 대한 생리적 반응과 선호도가 노년층과 청년층에서 어떠한 차이가 있는지 살펴보고자 하였다. 20대 및 60대 피험자에게 100％의 Basil oil, Jasmin oil, Lavender oil, Lemon il, Ylangylang oil을 제시하여 자율신경계의 반응인 심전도의 RR간격과 GSR을 측정ㆍ분석하였으며, 선호도 평가를 하여 자율신경계의 반응과의 상관성을 검토하였다. 20대는 Lemon 향에서 RR 간격 및 GSR의 반응이 안정되게 나타났으며, 선호도 평가에서도 Lemon 을 가장 선호도가 높게 평가하는 것을 볼 수 있었다. 60대는 Lavender에서 RR 간격이 증가하고 GSR이 감소하여 안정된 반응을 보였고, Lavender에 대한 선호도 또한 높게 평가하여 60대는 다른 향보다 Lavender를 좋은 향으로 평가함을 알 수 있었다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.449-467
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• 2003

뇌전도(EEG), 심전도(ECG) 와 같은 생체 전기신호는 카오스적 특성을 가지고 있으므로, 신호특성 분석에 비선형 도구를 사용하므로 의미있는 정보를 얻을 수 있다. 분석하는 데에는 주파수 특성, 변이 특성과 같은 생체시스템의 상태를 검증하는 방법이 주로 이용되어 왔다. 이에 본 연구에서는 심장 맥파의 RR 간격의 값을 획득하여 비선형 분석하는 도구로 Hurst Exponent 값의 변화를 모델화 하여 두 개의 비교대상 집단을 대상으로 차별성을 검출하는데 그 목적이 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.227-232
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• 2011

The QRS complex in ECG analysis is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. This paper presents the preprocessor method to detect R-peak, RR interval, and HRV in wireless sensor node. The derivative of the electrocardiogram is efficiency of preprocessing method for resource hungry wireless sensor node with low computation. We have implemented R-peak and RR interval detection application based on dECG for wireless sensor node. The sensor node only transfers meaning parameter of ECG. Thus, implementation of sensor node can save power, reduce traffic, and eliminate congestion in a WSN.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.107-116
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• 2009

This paper presents an approach to detect arrhythmia using heart rate variability and a fuzzy neural network. The proposed algorithm diagnoses arrhythmia using 32 RR-intervals that are 25 seconds on average. We extract six statistical values from the 32 RR-intervals, which are used to input data of the fuzzy neural network. This paper uses the neural network with weighted fuzzy membership functions(NEWFM) to diagnose arrhythmia. The NEWFM used in this algorithm classifies normal and arrhythmia. The performances by Tsipouras using the 48 records of the MIT-BIH arrhythmia database was below 80% of SE(sensitivity) and SP(specificity) in both. The detection algorithm of arrhythmia shows 88.75% of SE, 82.28% of SP, and 86.31% of accuracy.

• Journal of Yeungnam Medical Science
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• v.8 no.2
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• pp.52-62
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• 1991

The toxic effect of azalea extract, expecially on cardiovascular system, is relatively unclear. The purpose of this study is to study the possible underlying mechanism and effect of toxic ingredient of azalea on cardiovascular system. The 71 healthy rabbits were divided into 10 groups : In group as preliminary study ; 4cc of normal saline was administered intravenously(N) ; 0.7gm/kg and 1.0gm/kg of azalea extract was administered respectively in the same route, volume(A1, A2) ; atropine was administered intravenously(A) ; after pretreatment with atropine(0.04mg/kg) to block parasympathetic system, azalea extract was injected like the above groups(AA1, AA2) ; normal saline, 0.7gm/kg and 1.0gm/kg of azalea extract were administered respectively with 0.2cc(1 : 1000) epinephrine(E0,E1,E2). We measured the following indices at I minute interval during first 10 minutes and then 10 minute interval during next 30 minutes : RR interval, QTc interval, maximal systolic and diastolic pressure drop with occuring time and presence of significant arrhythmia. The results were as follows : 1. The changes of RR interval, QTc interval were significantly increased in groups by Azalea extract. The blood pressure change was significantly decreased in groups by Azalea extract. There were no significant differences according to dosage of Azalea extract. 2. The changes of RR interval, blood pressure were significant differences between administration of atropine and Azalea extract after pretreatment with atropine, but not in the change of QTc interval. 3. There were no significant differences in the change of RR interval, ATc interval, blood pressure drop according to pretreatment with atropine. 4. The interaction between epineprine and Azalea extract was not noted by the effect of epineprine itself. 5. The ST change by 0.7gm/kg, 1.0gm/kg of Azalea extract was revealed in 1 case(14.0%), 7 case(100%), respectively. 6. Most of all cases with arrhthymia, ventricular tachycardia, ventricular fibrillation, were noted in the group by epineprine, except one case by Azalea extract(1.0gm/kg). It was idioventricular rhythm. In conclusion, azalea extract has negative inotropic and chronotropic effect with arrhythmogenic potential possibly through direct myocardial ischemia or injury but we cann't be absolutely exclusive of actions of autonmic nervous system, especially parasympathetic nervous system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1643-1646
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• 1997

In this study, the method is proposed, which enable us to noninvasively assess baroreflex sensitivity through the closed-loop feedback modle between RR flucturarion and arterial blood pressure fluctuation. The proposed indexes of baroreflex sensitivity, BRS$_{LF}$와 BRS$_{HF}$ are calculated by the modulus (or gain) of the transfer function between fluctuatuons in blood pressure and RR interval in the LF band HF band, where the coherence is more than 0.5 to evaluate the performance of the proposed method, it is applied to various cardiovascular variability signals obtained form subjects under the submaximal ecericse on bicycle ergometner. In result it is concluded that the proposed method can noninvasively assess the baroreflex sensitivity.ty.