• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.201-204
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• 2010

In the mobile healthcare system, a very important vital sign in analyzing the status of user health is the HRV (heart rate variability). The used signals for measuring the HRV are electrocardiograph and PPG (photoplethysmograph). In extracting the HRV from the PPG signal, an important issue is that extract the exactly HRV from PPG signal distorted from the user's movements. This study suggested a design method of the Kalman filter to solve the problem, and evaluated the performances of a proposed method by PPG signals containing motion artifacts. In the results of experiments that compared with a variable step size adaptive filter proposed in recently, the proposed method showed better performance than an adaptive filter.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.216-222
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• 2013

It is known that blood circulation in human body causes the skin tone to change concurrently with heartbeats. A number of apps have been developed to measure the heartbeat using smartphone camera; however, no any further analysis is performed. In this paper we propose an algorithm that detects heartbeats from the phone's camera and further extracts the heart rate variability (HRV). We compare the HRV extracted from the camera with the HRV extracted from the electrocardiogram. We estimated a number of commonly used HRV characteristics and compared them. Our results show that smartphone camera leads to slightly overestimated characteristics although the difference in extracted HRV signals is negligible. As a consequence we suggest that a smartphone camera can be employed in a quick heart diagnosis and diagnosis of autonomic nervous system.

• 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.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.33-41
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• 2018

The purpose of this study is to develop more accurate and robust emotion prediction neural network (EPNN) model by combining heart rate variability (HRV) and neural network. For the sake of improving the prediction performance more reliably, the proposed EPNN model is based on various types of activation functions like hyperbolic tangent, linear, and Gaussian functions, all of which are embedded in hidden nodes to improve its performance. In order to verify the validity of the proposed EPNN model, a number of HRV metrics were calculated from 20 valid and qualified participants whose emotions were induced by using money game. To add more rigor to the experiment, the participants' valence and arousal were checked and used as output node of the EPNN. The experiment results reveal that the F-Measure for Valence and Arousal is 80% and 95%, respectively, proving that the EPNN yields very robust and well-balanced performance. The EPNN performance was compared with competing models like neural network, logistic regression, support vector machine, and random forest. The EPNN was more accurate and reliable than those of the competing models. The results of this study can be effectively applied to many types of wearable computing devices when ubiquitous digital health environment becomes feasible and permeating into our everyday lives.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.50-55
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• 2015

The noise of MRI shooting is 100dB loud and has an intensive psychological and physiological influences on the human body. ECG signals were measured by experimental methods, while wearing earplugs for 15 minutes in the stable state. Then the ECG signals were measured for 30 minutes while listening to about 100dB of sound in a MRI equipment. In this study, the heart rate variability of men and women was analyzed according to the MRI noise stress level through the frequency analysis. As the MRI noise level is about 100dB, HRV analysis resulted in an imbalance between the sympathetic and parasympathetic. During the period from the resting state up to 10 minutes, the maximum stress state was shown. This study will encourage MRI workers to take interests in hearing protection for the patient and to make objective indicators about MRI noises.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.289-292
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• 2000

선형 자귀회귀(AR) 모델을 근거로한 HRV 파워 스펙트럼해석은 비침습적으로 자율신경의 반응을 정량화하는데 폭넓게 사용된다. 본 연구는 단구간(2분 미만)의 심전도와 맥파 신호로부터 시계열 HRV의 파워스펙트럼을 추정한다. 시계열은 정상인을 대상으로 검출한 심전도와 맥파신호의 특징점 시간간격(RRI, PPI)으로부터 구하였다. 발생된 시계열은 다항식 보간법에 의해 AR모델에 적합하게 재구성하였으며, AR모델 계수는 Burg법에 의해 계산하였다. AR모델을 적용한 단구간의 심전도와 맥파의 심박변동에 대한 파워스펙트럼밀도는 저주파수(LF)와 고주파수(HF)에서 매끄러운 스펙트럼 파워를 나타내고 있다. 또한 동일한 피험자의 심전도와 맥파의 파워스펙트럼밀도를 비교한 결과 동일한 모양을 나타내었다.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.71-82
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• 2018

Stress involves changes in behavior, autonomic function and the secretion of hormones. Autonomic nervous system (ANS) contributes to physiological adaptive process in short durations. In particular, heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the ANS activity related to mental stress. The aim of this study is to investigate correlations between psychological responses to stress and HRV indices induced by the cognitive stressor. Thirty-three participants rated their mental and physical symptoms occurred during the past two weeks on Stress Response Inventory (SRI), which is composed of seven stress factors that may influence the status of mental stress levels. Then, they underwent the psychophysiological procedures, which are collected electrocardiogram (ECG) signals during a cognitive stress task. HRV indices, the standard deviation of R-R interval (SDNN), root mean square of successive R-R interval difference (RMSSD) and low frequency (LF)/high frequency (HF) ratio were extracted from ECG signals. Physiological responses were calculated stress responses by subtracting mean of the baseline from the mean of recovery. Stress factors such as tension, aggression, depression, fatigue, and frustration were positively correlated to HRV indices. In particular, aggression had significant positive correlations to SDNN, RMSSD and LF/HF ratio. Increased aggressive responses to stress correlated with the increases of all HRV indices. This means the increased autonomic coactivation. Additionally, tension, depression, fatigue, and frustration were positively associated with RMSSD reflecting increases in parasympathetic activation. The autonomic coactivation may represent an integrated response to specific cognitive reactions such as the orienting response.

• 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.

• ETRI Journal
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• v.37 no.2
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.517-523
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• 2019

The aim of the study was to examine the effects of the five-sensory horticulture therapy on perceived stress and HRV in adults with depression. The participants were 26 (experimental group=13, control group=13) adult with depression and enrolled in the Regional C mental health welfare center from April to June 2018. The experimental group participated in a total of 8-session five sensory horticultural therapy once a week, and the control group provided horticultural therapy once to the desired subjects after the program. The data were analyzed using descriptive statistics, t-test, and Mann-Whitney U test. There were statistically significant change in perceived stress (t = 3.11, p = .005), LF (t = -3.39, p = .002) and SDNN (t = -2.48, p = .025) in experimental group compared to the control group. Therefore, this therapy was effective for reduction of stress among individuals with the depressive disorder.