• 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 Biomedical Engineering Research
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• v.31 no.4
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• pp.316-320
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• 2010

HRV(Heart rate variability) analysis parameter is widely used as an index to evaluate the autonomic nervous system and cardiac function. For reliable HRV analysis, we need to acquire the accurate ECG signals. Most of commercially available portable ECG devices have low sampling rate because of low power consumption and small size issues, which make it difficult to measure RR-interval accurately. This study is to improve the accuracy of RR-interval by developing R-wave interpolation technique, based on the morphological characteristics of the QRS complex. When the developed method was applied to ECG obtained at 200 Hz and the results were compared with 1000 Hz reference device, the error range decreased by 1.33 times in sitting and by 2.38 times in cycling exercise. Therefore, the proposed interpolation technique is thought to be useful to improve the accuracy of R-R interval in the portable ECG device with low sampling rate.

• 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.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.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1276-1279
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• 1987

Cardiac arrhythmias are associated with electrical Instability and, hence, with abnormal mechanical activity of the heart in many cases, arrhythmias can be treated with drugs or electric shock to control and/or stop them. Hence fast arrhythmia detection is very important. In this paper RR interval, QRS width, and morphology are used for diagnosis and QRS complex is detected by hardware system. hence diagnosing time is shorten. Moreover doctors or nurses who have little knowledge of computer manipulation can get the Information of Patient's ECG by showing characteristics of abnormal waveform and by mapping graphs of RR interval .vs. QRS width and RR interval .vs. morphology on screen.

• 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.

• 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 KSMRM Conference
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• 2003.10a
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• pp.44-44
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• 2003

To determine feasibility of contrast-enhanced MRI (co-MRI) at true end-diastole (ED) free from limitation of time for inversion-recovery and trigger window for quantifying transmural extent of infarction. 대상 및 방법： MRI was performed in 18 patients with myocardial infarction. Cine imaging and co-MRI with same registered slices in short axis were peformed. To allow true ED co-MRI, ECG synchronization should use two RR-intervals for one acquisition of a segment of k-space by setting the heart rate to half that of the true heart rate. Trigger delay time was adjusted to the RR-interval for imaging at ED and to the sum of RR-interval plus the time between R-wave and the end-systole (ES) determined in cine images for imaging at ES.

• Proceedings of the KSMRM Conference
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• 2003.10a
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• pp.91-91
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• 2003

To determine feasibility of contrast-enhanced MRI (co-MRI) at true end-diastole (ED) free from limitation of time for inversion-recovery and trigger window for quantifying transmural extent of infarction. 대상 및 방법： MRI was peformed in 18 patients with myocardial infarction. Cine imaging and co-MRI with same registered slices in short axis were performed. To allow true ED co-MRI, ECG synchronization should use two RR-intervals for one acquisition of a segment of k-space by setting the heart rate to half that of the true heart rate. Trigger delay time was adjusted to the RR-interval for imaging at ED and to the sum of RR-interval plus the time between R-wave and the end-systole (ES) determined in cine images for imaging at ES.