• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.545-551
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• 2022

This study analyzed the correlation between stress measurements calculated through HRV signals and thyroid function test items. 181 healthy adults without disease who visited Clinic K were the subjects of this study. Stress resistance (SR) and stress index (SI) were calculated using the acquired HRV signal, and TSH, Free T4, and T3 were used as thyroid function test items. For the measured values, the relationship between each item was statistically analyzed through Pearson correlation analysis. From the results, it was confirmed that Free T4 and SR had a positive correlation (r=0.18) and a negative correlation with SI (r=-0.16). Through this, it was confirmed that there is a significant relationship between thyroid function and HRV signal.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2010-2012
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• 2008

This study describes a system for human sensibility evaluation using PPG(photoplethysmogram) signal and biofeedback algorithm to respond the bad(negative) mood. For this objective, PPG signals for two emotional states(positive/negative) are collected. To evoke the test emotions, happy(or joyful) and sad(or irritating) movie files are collected and played in subjects' monitor. From the acquired PPG signal, the heart rate variability(HRV) is calculated. Using the HRV and its FFT spectra, the human sensibility is evaluated. Since the heart is a representative organ which is controlled by the autonomic nervous system(ANS), the ANS may reflect the changes in emotion. The biofeedback algorithm is designed with motion image player interacting with the results of the sensibility evaluation. It was shown that HRV was changed according to the subject's emotions. Accordingly, the sensibility evaluation test showed feasibility of the our method.

• International Journal of Vascular Biomedical Engineering
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• v.3 no.2
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• pp.17-24
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• 2005

The main purpose of our study is to propose a new methodology to develop the multi-parametric measure including linear and nonlinear measures of heart rate variability diagnosing cardiovascular disease. We recorded electrocardiogram for three recumbent postures; the supine, left lateral, and right lateral postures. Twenty control subjects (age: $56.70{\pm}9.23$ years), 51 patients with angina pectoris (age: $59.98{\pm}8.41$ years) and 13 patients with acute coronary syndrome (age: $59.08{\pm}9.86$ years) participated in this study. To develop the multi-parametric measure of HRV, we used the multiple discriminant analysis method among statistical techniques. As a result, the multiple discriminant analysis gave 75.0% of goodness of fit. When the linear and nonlinear measures of HRV are individually used as a clinical tool to diagnose cardiac autonomic function, there is quite a possibility that the wrong results will be obtained due to each measure has different characteristics. Although our study is a preliminary one, we suggest that the multi-parametric measure, which takes into consideration the whole possible linear and nonlinear measures of HRV, may be helpful to diagnose the cardiovascular disease as a diagnostic supplementary tool.

• Progress in Medical Physics
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• v.10 no.3
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• pp.141-149
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• 1999

This research analyzed the HRV signals by using wavelet transform to observe the activities of autonomous nervous system in a sleep state. This research also restructured the HRV signals from electrocardiogram and by using coefficient which was obtained through wavelet transform, analyzed the signals by frequency bandwidth. Then compared the analyzed results with existing frequency analyzing method using AR model techniques. The suggested wavelet coefficient from power spectrum component in the study shows a similar tendency with the results from FFT or AR model technique. Therefore, it can be found that power spectrum analyzing method by wavelet coefficient is a useful as a tool for analyzing autonomous nervous system activities using HRV signals. Since the suggested method able to clearly depict the progression of change in time zone, which was once impossible with the existing methods, it is presumed that it will be useful in other physiological signals.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.1-9
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• 2012

This paper presents an algorithm for depression diagnosis using the Neural Network with Weighted Fuzzy Membership functions (NEWFM) and heart rate variability (HRV). In the algorithm, 22 different features were initially extracted from the HRV signal by frequency domain, time domain, wavelet transformed, and Poincar$\acute{e}$ transformed feature extraction methods; of these 6 optimal features were selected by significance evaluation using Non-overlap Area Distribution Measurement (NADM) based on NEWFM. The proposed algorithm uses these 6 optimal features to diagnose depression with an accuracy of 95.83%.

• Science of Emotion and Sensibility
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• v.14 no.2
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• pp.269-278
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• 2011

This paper illustrates the inter-relationship between the theta/alpha ratio of the EEG signal and multiple HRV related parameters associated with the cardiovascular system response during event-related stimuli. Both EEG and PPG signals were simultaneously recorded in 21 healthy subjects. All subjects had their attention focused on the CNT program for nine minutes. Time-frequency analysis was applied to the EEG and PPG signals. The theta/alpha ratio was extracted from the EEG results, and the HRV features, including beat interval(1), SDNN(2), RMSSD(3), NN50(4), LF(5), HF(6), and LFIHF(7), were extracted from the PPG. Through multiple linear regression, the relationship ($R^2$) between the multiple combined features and the theta/alpha rhythm was identified. As a result, the combinations of $R^2$($R^2=0.253$; seven dimensions) and the theta/alpha ratio indicated a higher inter-relationship value than those of other combinations. The combinations of features that were greater than three dimensions, based on {SDNN(2), HF(6)}, generally showed higher $R^2$ value. We demonstrate that the high dimensional combinations had a higher correlation than did the low dimensional combinations.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.543-543
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• 2000

A new method for HRV(heart rate variability) detection from the R-wave signal, based on the IPFM(integral pulse frequency modulation) model and its similarity to pulse position modulation, is presented. The proposed method exert lowpass filtering with a Kaiser window. In this paper, The proposed method presents a powerful, but simple, tool for investigation of HRV. It also guarantees real-time behavior. simplicity in design, and phase linearity. Even without the basic assumption of IPFM model. the new algorithm can still be used on-line and with higher performance. It is thoroughly proved that lowpass filtering is an ideal method for PSD(Power Spectrum Density) analysis of HRV.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1906-1908
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• 2007

This study describes a software engine that can evaluate human sensibility using a heart rate variable(HRV) of hypochodriac or old people, and suggest biofeedback to enhance their emotion. To develop the software engine, using PPG signal heart rate and HRV are calculated. Using the FFT spectra of HRV, human sensibility is estimated. And a biofeedback software is designed with motion image player, breathing control and other function modules.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.935-939
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• 2003

The emotion recognition is one of the most significant interface technologies which make the high level of human-machine communication possible. The central nervous system stimulated by emotional stimuli affects the autonomous nervous system like a heart, blood vessel, endocrine organs, and so on. Therefore bio-signals like HRV, ECG and EEG can reflect one' emotional state. This study investigates the correlation between emotional states and bio-signals to realize the emotion recognition. This study also covers classification of human emotional states, selection of the effective bio-signal and signal processing. The experimental results presented in this paper show possibility of the emotion recognition.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3233-3235
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• 2000

A new method for HRV(heart rate variability} detection from the R-wave signal, based on the IPFM (integral pulse frequency modulation) model and its similarity to pulse position modulation, is presented. The proposed method exert lowpass filtering with a Kaiser window. In this paper, The proposed method presents a powerful, but simple, tool for investigation of HRV. It also guarantees real-time behavior, simplicity in design, and phase linearity. Even without the basic assumption of IPFM model, the new algorithm can still be used on-line and with higher performance. It is thoroughly proved that lowpass filtering is an ideal method for PSD (Power Spectrum Density) analysis of HRV.