• Journal of Biomedical Engineering Research
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• v.24 no.3
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• pp.151-158
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• 2003

Numerous studies of short-term, beat-to-beat variability in cardiovascular signals have used linear analysis techniques. However, no study has been done about the appropriateness of linear techniques or the comparison between linearities and nonlinearities in short-term, beat-to-beat variability. This paper aims to verify the appropriateness of linear techniques by investigating nonlinearities in short-term, beat-to-beat variability. We compared linear autoregressive moving average(ARMA) with nonlinear neural network(NN) models for predicting current instantaneous heart rate(HR) and mean arterial blood pressure(BP) from past HRs and BPs. To evaluate these models. we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that 10 technique provides adequate characterization of the system dynamics responsible for generating short-term, beat-to-beat variability.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.799-804
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• 2015

This study aims at the new analysis of heart beat fluctuations by applying physiological Heart Rate Variability Model with representing the cardiac control system in sympathetic and parasympathetic-coupling oscillator constants, C_s and C_p. To find the effects of coupling constants on the beat-to-beat fluctuations, Integral Pulse Frequency Modulation (IPFM) model is adopted to generate the time series data of ECG R-peaks and represent them by poincaré scattergram plot in the time domain and HRV in the frequency domain, respectively. The actual poincaré plots and HRV spectrum are also analyzed by acquiring the experimental data from the subjects exposed to the emotional-stress invoking environment and the function of the coupling constants are verified in terms of antagonism in sympathetic and parasympathetic activity.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.198-205
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• 2005

In this paper, we proposed a new algorithm for the separation of fetal ECG from single channel abdominal ECG. The algorithm consists of a stage of demixing vector calculation for initial signal and a stage of fetal beat detection for the rest of signal. The demixing vector was obtained by applying independent component analysis technique to projected signals into time-frequency domain. For the test of this algorithm, simulation signals, De Lathauwer's data and some measured data, which was acquired from 8 healthy volunteers whose pregnant periods ranged from 22 weeks to 35 weeks and whose ages from 27 to 37, were used. For each data, the accuracy of fetal beat detection was $100\%$ and with the location of fetal beats, fetal heart rate variability and morphology could be offered. In conclusion, this proposed algorithm showed the possibility of fetal beat separation with a single channel abdominal ECG and it might be adopted to a fetal health monitoring system, by which a single channel abdominal ECG is acquired.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.794-798
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• 2018

There is a growing need for a care system that can continuously monitor, manage and effectively relieve stress for modern people. In recent years, mobile healthcare devices capable of measuring heart rate have become popular, and many stress monitoring techniques using heart rate variability analysis have been actively proposed and commercialized. In addition, respiratory biofeedback methods are used to provide stress relieving services in environments using mobile healthcare devices. In this case, breathing information should be measured well to assess whether the user is doing well in biofeedback training. In this study, we extracted the heart beat interval signal from the PPG and used the oscillator based notch filter based on the IIR band pass filter to track the strongest frequency in the heart beat interval signal. The respiration signal was then estimated by filtering the heart beat interval signal with this frequency as the center frequency. Experimental results showed that the number of breathing could be measured accurately when the subject was guided to take a deep breath. Also, in the timeing measurement of inspiration and expiration, a time delay of about 1 second occurred. It is expected that this will provide a respiratory biofeedback service that can assess whether or not breathing exercise are performed well.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1947-1954
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• 2013

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. But it is difficult to detect the P and T wave signal because of person's individual difference. Therefore it is necessary to design efficient algorithm that classifies different arrhythmia in realtime and decreases computational cost by extrating minimal feature. In this paper, we propose arrhythmia detection based on binary coding using QRS feature varibility. For this purpose, we detected R wave, RR interval, QRS width from noise-free ECG signal through the preprocessing method. Also, we classified arrhythmia in realtime by converting threshold variability of feature to binary code. PVC, PAC, Normal, BBB, Paced beat classification is evaluated by using 39 record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 97.18%, 94.14%, 99.83%, 92.77%, 97.48% in PVC, PAC, Normal, BBB, Paced beat classification.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.19 no.2
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• pp.75-90
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• 2015

Objectives The purposes of this study was to develope a Qi gong exercise that suits characteristics of post mastectomy women, and to evaluate the effect of Qi gong exercise on immune response, blood circulation index, pulse wave parameter and heart rate variability. Methods This study was applied to total 35 post mastectomy women, including 17 for experiment group and 18 for control group. The Qi gong exercise was composed of total 24 times of 90 minutes per each time, twice a week, and 12 weeks and it was conducted by the oriental medicine professor who was an expert of Qi gong exercise. Results 1. Two group comparison revealed that the experimental group had significantly improved immune response(p<.021), HR(beats/min)(p<.001), ESV(ml/beat)(p=.038), ESI($ml/beat/m^2$)(p=.040), ECO (L/min)(p=.019), ECI($L/min/m^2$)(p=.023), ECRI($dyne^*sec/cm$)(p=.015), Left Kwan($div^3$)(p=.021), Right Kwan($div^3$)(p=.038), Mean HRV(cycle/min)(p<.001), SDNN(ms)(p=.043), RMSSD(ms)(p=.040), and TP(log $ms^2$)(p=.039). 2. Two group comparison revealed that the experimental group had significantly decreased ECR ($dyne^*sec^*cm^{-5}$) (p=.034), Left RAI(p=.044), Right RAI(p=.042), and pNN50(%)(p=.038). Conclusions These results from Qi gong exercise program can be used as basic data for development of health promotion program for Post Mastectomy Women.

• Perspectives in Nursing Science
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• v.14 no.2
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• pp.64-69
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• 2017

Purpose: Breathing can be controlled either unconsciously or consciously. In Asian countries, various conscious breathing-control techniques have been practiced for many years to promote health and wellbeing. However, the exact mechanism underlying these techniques has not yet been established. The purpose of this study is to explore the physiological mechanism explaining how conscious breathing control could affect the autonomic nervous system, brain activity, and mental changes. Methods: The coupling phenomenon among breathing rhythm, heart rate variability, and brain waves was explored theoretically based on the research hypothesis and a review of the literature. Results: Respiratory sinus arrhythmia is a well-known phenomenon in which heart rate changes to become synchronized with breathing: inhalation increases heart rate and exhalation decreases it. HRV BFB training depends on conscious breathing control. During coherent sinusoidal heart rate changes, brain ${\alpha}$ waves could be enhanced. An increase in ${\alpha}$ waves was also found and the synchronicity between heart beat rhythm and brain wave became strengthened during meditation. Conclusion: In addition to the effect of emotion on breathing patterns, conscious breathing could change heart beat rhythms and brainwaves, and subsequently affect emotional status.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.493-498
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• 2016

Heart Rate Variability (HRV) represents beat-to-beat fluctuations of R-R intervals in Electrocardiogram (ECG). On of the clinical applications of HRV is to assess the mental-stress state by evaluating its power spectral density distribution. This study aims at finding new discriminative role of the coupled-oscillating coupling constants, Cs and Cp in the Integral Pulse Frequency Modulation (IPFM) model. Based on comparing with power spectral density of HRV in terms of the relative ratio of the low and high-frequency power component, we can conclude the fact that the coupling parameters Cs and Cp can replace the role of HRV power spectrum interpretation for judging the mental-stress state.

• Sleep Medicine and Psychophysiology
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• v.9 no.1
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• pp.24-33
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• 2002

Objectives: Nasal continuous positive airway pressure (CPAP) corrected elevated blood pressure (BP) in some studies of obstructive sleep apnea syndrome (OSAS) but not in others. Such inconsistent results in previous studies might be due to differences in factors influencing the effects of CPAP on BP. The factors referred to include BP monitoring techniques, the characteristics of subjects, and method of CPAP application. Therefore, we evaluated the effects of one night CPAP application on BP and heart rate (HR) reactivity using non-invasive beat-to-beat BP measurement in normotensive and hypertensive subjects with OSAS. Methods: Finger arterial BP and oxygen saturation monitoring with nocturnal polysomnography were performed on 10 OSAS patients (mean age $52.2{\pm}12.4\;years$; 9 males, 1 female; respiratory disturbance index (RDI)>5) for one baseline night and another CPAP night. Beat-to-beat measurement of BP and HR was done with finger arterial BP monitor ($Finapres^{(R)}$) and mean arterial oxygen saturation ($SaO_2$) was also measured at 2-second intervals for both nights. We compared the mean values of cardiovascular and respiratory variables between baseline and CPAP nights using Wilcoxon signed ranks test. Delta ($\Delta$) BP, defined as the subtracted value of CPAP night BP from baseline night BP, was correlated with age, body mass index (BMI), baseline night values of BP, BP variability, HR, HR variability, mean $SaO_2$ and respiratory disturbance index (RDI), and CPAP night values of TWT% (total wake time%) and CPAP pressure, using Spearman's correlation. Results: 1) Although increase of mean $SaO_2$ (p<.01) and decrease of RDI (p<.01) were observed on the CPAP night, there were no significant differences in other variables between two nights. 2) However, delta BP tended to increase or decease depending on BP values of the baseline night and age. Delta systolic BP and baseline systolic BP showed a significant positive correlation (p<.01), but delta diastolic BP and baseline diastolic BP did not show a significant correlation except for a positive correlation in wake stage (p<.01). Delta diastolic BP and age showed a significant negative correlation (p<.05) during all stages except for REM stage, but delta systolic BP and age did not. 3) Delta systolic and diastolic BPs did not significantly correlate with other factors, such as BMI, baseline night values of BP variability, HR, HR variability, mean SaO2 and RDI, and CPAP night values of TWT% and CPAP pressure, except for a positive correlation of delta diastolic pressure and TWT% of CPAP night (p<.01). Conclusions: We observed that systolic BP and diastolic BP tended to decrease, increase or remain still in accordance with the systolic BP level of baseline night and aging. We suggest that BP reactivity by CPAP be dealt with as a complex phenomenon rather than a simple undifferentiated BP decrease.