• Journal of Biomedical Engineering Research
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• v.39 no.2
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• pp.69-79
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• 2018

The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ${\delta}RR$ interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.623-628
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• 2004

This paper describes autonomic nervous system activities caused by hyperthermia of far-infrared ray on human body. Designed protocol and analysis algorithm were evaluated by experiments on 20 subjects to analyze the characteristic of heart rate variability(HRV) signals which could be analyzed by FFT power spectrum and time-frequency analysis. Using Poincare' plot analysis, LF and HF were compared with SD1 and SD2. During the experiment, subject was exposed to hyperthermic effects of far-infrared radiation. We could confirm that far-infrared ray, which was known to improve the blood circulation, stress state and enhancing thermal effect into human body, had an effect on human nervous system. As the hyperthermic temperature of far-infrared ray increased, the activity of cardiovascular system to sustain the homeostasis was observed by means of investigating the increase of the sympathetic activity.

• Journal of Biomedical Engineering Research
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• v.24 no.1
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• pp.1-8
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• 2003

The job of long distance driving is likely to be fatiguing and requires long period alertness and attention, which make considerable demands of the driver. Driving fatigue contributes to driver related with accidents and fatalities. In this study, we investigated the relationship between the number of hours of driving and driving fatigue using heart rate variability(HRV) signal. With a more traditional measure of overall variability (standard deviation, mean, spectral values of heart rate). Nonlinear characteristics of HRV signal were analyzed using Approximate Entropy (ApEn) and Poincare plot. Five subjects drive the four passenger vehicle twice. All experiment number was 40. The test route was about 300Km continuous long highway circuit and driving time was about 3 hours. During the driving, measures of electrocardiogram(ECG) were performed at intervals of 30min. HRV signal, derived from the ECG, was analyzed using time, frequency domain parameters and nonlinear characteristic. The significance of differences on the response to driving fatigue was determined by Student's t-test. Differences were considered significant when a p value < 0.05 was observed. In the results, mean heart rate(HRmean) decreased consistently with driving time, standard deviation of RR intervals(SDRR), standard deviation of the successive difference of the RR intervals(SDSD) increased until 90min. Hereafter, they were almost unchanging until the end of the test. Normalized low frequency component $(LF_{norm})$, ratio of low to high frequency component (LF/HF) increased. We used the Approximate Entropy(ApEn), Poincare plot method to describe the nonlinear characteristics of HRV signal. Nonlinear characteristics of HRV signals decreased with driving time. Statistical significant is appeared after 60 min in all parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1072-1077
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• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.65-72
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• 2017

Premature Ventricular Contraction(PVC) arrhythmia is most common abnormal-heart rhythm that may increase mortal risk of a cardiac patient. Thus, it is very important issue to identify the specular portraits of PVC pattern especially from the patient. In this paper, we propose a new method to extract the characteristics of PVC pattern by applying K-means machine learning algorithm on Heart Rate Variability depicted in Poinecare plot. For the quantitative analysis to distinguish the trend of cluster patterns between normal sinus rhythm and PVC beat, the Euclidean distance measure was sought between the clusters. Experimental simulations on MIT-BIH arrhythmia database draw the fact that the distance measure on the cluster is valid for differentiating the pattern-traits of PVC beats. Therefore, we proposed a method that can offer the simple remedy to identify the attributes of PVC beats in terms of K-means clusters especially in the long-period Electrocardiogram(ECG).

• Coupled systems mechanics
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• v.2 no.2
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• pp.159-174
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• 2013

The present work aims at investigating the nonlinear dynamics, bifurcations, and stability of an axially accelerating beam with an intermediate spring-support. The problem of a parametrically excited system is addressed for the gyroscopic system. A geometric nonlinearity due to mid-plane stretching is considered and Hamilton's principle is employed to derive the nonlinear equation of motion. The equation is then reduced into a set of nonlinear ordinary differential equations with coupled terms via Galerkin's method. For the system in the sub-critical speed regime, the pseudo-arclength continuation technique is employed to plot the frequency-response curves. The results are presented for the system with and without a three-to-one internal resonance between the first two transverse modes. Also, the global dynamics of the system is investigated using direct time integration of the discretized equations. The mean axial speed and the amplitude of speed variations are varied as the bifurcation parameters and the bifurcation diagrams of Poincare maps are constructed.

• The Korean Journal of Pain
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• v.22 no.1
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• pp.39-46
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• 2009

Background: Ketamine has an indirect sympathetic stimulation effect. We investigated heart rate variability (HRV) as a marker of cardiac autonomic function after a target controlled infusion (TCI) of ketamine with a plasma concentration of 30 or 60 ng/ml. Methods: In 20 adult volunteers, the mean of the R wave to the adjacent R wave interval (RRI), the range of RRI, the root mean square successive difference of intervals (RMSSD), the total power, the low frequency (LF, 0.04-0.15 Hz) power, the high frequency (HF, 0.15-0.4 Hz) power, the normal unit HF (nuHF), the normal unit LF (nuLF), the LF/HF ratio and the SD1 and the SD2 in the Poincare plot were measured before and after a TCI of ketamine. We observed for any psychedelic symptoms or sedation. Results: There were no differences in the mean and range of the RRI, RMSSD, total power, LF power, HF power, nuHF, nuLF, LF/HF ratio, SD1 and SD2 between before and after ketamine administration. The OAA/S score was higher and there were more psychedelic symptoms with a 60 ng/ml plasma concentration than with a 30 ng/ml plasma concentration. Conclusions: This study did not show any effect of a low plasma concentration of ketamine on the autonomic nervous system.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.61-67
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• 2016

This paper presented a method for random forest based the arrhythmia classification using both heart rate (HR) and heart rate variability (HRV) features. We analyzed the MIT-BIH arrhythmia database which contains half-hour ECG recorded from 48 subjects. This study included not only the linear features but also non-linear features for the improvement of classification performance. We classified abnormal ECG using mean_NN (mean of heart rate), SD1/SD2 (geometrical feature of poincare HRV plot), SE (spectral entropy), pNN100 (percentage of a heart rate longer than 100 ms) affecting accurate classification among combined of linear and nonlinear features. We compared our proposed method with Neural Networks to evaluate the accuracy of the algorithm. When we used the features extracted from the HRV as an input variable for classifier, random forest used only the most contributed variable for classification unlike the neural networks. The characteristics of random forest enable the dimensionality reduction of the input variables, increase a efficiency of classifier and can be obtained faster, 11.1% higher accuracy than the neural networks.

• Journal of Veterinary Clinics
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• v.26 no.3
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• pp.212-219
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• 2009

Anticholinergics, which are commonly given as a pre-anesthetic medication to prevent adverse effects in canine anesthesia, can cause cardiac adverse effects. To determine the effects of atropine and glycopyrrolate on the balance of sympathetic nervous tone and parasympathetic nervous tone of the heart during ketamine anesthesia in beagle dogs, heart rate variability(HRV), duration of anesthesia and behavioral changes were evaluated. There were no significant temporal domain differences between atropine and glycopyrrolate. Concerning the frequency domain component, atropine and glycopyrrolate effects were significantly lower(P<0.05) than the control saline-treated group. However, the root mean square of the interval differences between consecutive R peaks(RMSSD) and the standard deviation of Poincare plot perpendicular to the line-of-identity(SD1) in atropine were significantly decreased(P<0.05) from the baseline value, and the low frequency/high frequency ratio(LF:HF ratio) in glycopyrrolate was significantly increased from baseline value(P<0.05). The change of SD1 agreed with that of the high frequency(HF) in the frequency domain component and also with those of respiratory rate and $SpO_2-R$. Our results prove that glycopyrrolate is more suitable as a pre-anesthetic anticholinergic in ketamine anesthesia of dogs with respect to safety and duration of action.