• KOREAN ASSOCIATION OF OCCUPATIONAL HEALTH NURSES
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• v.15 no.3
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• pp.38-38
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• 2008

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.387-392
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• 2018

The aim of this study was to determine the statistical significance of heart rate variability(HRV) between sleep stages, Apnea-hypopnea index(AHI) and age in patients with obstructive sleep apnea(OSA). This study evaluated the main parameters of HRV over time domain and frequency domain in 40 patients with sleep apnea. The non-REM(sleep stage) was statistically validated by comparing the AHI degree of the three groups(mild, moderate, severe) of sleep apnea patients. The NN50(p=0.043), pNN50(p=0.044), VLF peak(p=0.022), LF/HF(p=0.028) were statistically significant in the R-R interval of patients with sleep apnea from the control group (p<0.05). The LF / HF (p = 0.045) and HF power (p = 0.0395) parameters between the non-RAM sleep (sleep 2 phase) and REM sleep in patients with sleep apnea were statistically significant in the control group(p<0.05). We may be able to provide a basis for understanding the correlation among AHI, sleep stage and age and heart rate variability in patients with obstructive sleep apnea.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Horticultural Science & Technology
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• v.28 no.6
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• pp.1066-1071
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• 2010

To evaluate the effect of horticultural therapy (HT) on the emotional improvement of depressed patients, computer-based heart rate variability (HRV) was compared with self-report scale (SRS) known as existing subjective evaluation method. SRS included four test areas: mental stress scale (MSS), physical stress scale (PSS), Beck anxiety inventory (BAI), and Beck depression inventory (BDI). HRV was itemized into four parameters: standard deviation of the N-N intervals (SDNN), square root of mean squared difference of successive N-N intervals (RMSSD), total power (TP), and low-frequency/high-frequency ratio (LF/HF ratio). Thirty patients with depression at the same mental hospital participated in this study. 15 patients of the treatment group received HT once a week for three months, but the control group did not during the same period. As a result, the emotional improvement in treatment group was clearly identified through HRV as well as SRS. The significant difference was shown at three test areas (MSS, BAI, and BDI, $p$ < 0.001) in SRS and at one parameter (total power, $p$ < 0.05) in HRV. There was noticeable increase in SDNN, RMSSD, and LF/HF ratio in treatment group after HT activity, but no significant difference. Although all parameters of HRV did not show significance, the possibility of HRV as an objective evaluation method to HT was recognized in this study. These results also implied that HT was efficient in the mental and physical regeneration of the depressed patients in both subjective and objective evaluation methods.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1383-1402
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• 2017

This study aims to investigate the effects of aroma inhalation on pain, anxiety, and heart rate variability of elderly women with total knee arthroplasty (TKA) during continuous passive motion (CPM) exercise. Equivalent control group pretest-posttest design was used. Participants were randomized to intervention group (n=26) or control group (n=27). Participants inhaled aroma or distilled water for 5 minutes before CPM exercise and for 40 minutes during CPM exercise on 3 consecutive days. Pain NRS, number of painkiller used, anxiety NRS, blood pressure, and heart rate variability (HRV) were measured before and after intervention. Pain NRS, anxiety NRS, blood pressure in experimental group were significantly lower than those of control group. All indices of HRV were significantly different between the two groups. Based on these results, it can be concluded that aroma inhalation was effective in decreasing pain and anxiety and changing HRV among elderly women with TKA during CPM exercise.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.137-146
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• 2024

The purpose of this study was to determine the effects of 12 weeks of WB-EMS training on body composition and heart rate variability based on BMI Level in Women. The subjects of the study were premenopausal women, and they were classified into the BMI-N(n=15) group for BMI<25, the BMI-1(n=16) group for BMI=25~29.9, and the BMI-2(n=9) group for BMI>30. And then, WB-EMS training was performed of 3 times a week for 12 weeks. Body composition and HRV were measured before and after the participation in exercise, which were subjected to a repeated-measures two-way ANOVA. In the case of a significant interaction between time and group, paired sample t-tests were conducted for a post-hoc analysis within each subject group. Tukey's method was used for post-hoc testing of differences between groups, and the significance level was set at 0.5. The results were as follows; First, The effect of WB-EMS training was found in all variables of body composition. In particular, Weight, BMI, FFM, and FM decreased the most in the BMI-2 group, followed by the BMI-1 and BMI-N groups. %BF and VF decreased the most in the BMI-2 group. Second, There was a difference in BPM in all groups, and the BMI-2 group showed the greatest decrease. There were differences in SDNN and RMSSD for each group, and there was no difference according to obesity level. There was no difference in LF, HF, and LF/HF ratio. In conclusion, it was confirmed that WB-EMS training can be an exercise therapy that has a positive effect on the body composition change and cardiac circulatory system in women with a high level of obesity.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.105-112
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• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.257-269
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• 2021

The purpose of this study is to collect biosignal data in a non-invasive and non-restrictive manner using a BCG (Ballistocardiogram) sensor, and utilize artificial intelligence machine learning algorithms in ICT and high-performance computing environments. And it is to present and study a method for developing and validating a data-based blood glucose prediction model. In the blood glucose level prediction model, the input nodes in the MLP architecture are data of heart rate, respiration rate, stroke volume, heart rate variability, SDNN, RMSSD, PNN50, age, and gender, and the hidden layer 7 were used. As a result of the experiment, the average MSE, MAE, and RMSE values of the learning data tested 5 times were 0.5226, 0.6328, and 0.7692, respectively, and the average values of the validation data were 0.5408, 0.6776, and 0.7968, respectively, and the coefficient of determination (R²) was 0.9997. If research to standardize a model for predicting blood sugar levels based on data and to verify data set collection and prediction accuracy continues, it is expected that it can be used for non-invasive blood sugar level management.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.1195-1196
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• 2012