• 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.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.61-70
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• 2017

Physiological responses have been measured to recognize emotion. Although physiological responses have been interrelated between organs, their connectivities have been less considered for emotion recognizing. The connectivities have been assumed to enhance emotion recognition. Specially, autonomic nervous system is physiologically modulated by the interrelated functioning. Therefore, this study has been tried to analyze connectivities between heart and respiration and to find the significantly connected variables for emotion recognition. The eighteen subjects(10 male, age $24.72{\pm}2.47$) participated in the experiment. The participants were asked to listen to predetermined sound stimuli (arousal, relaxation, negative, positive) for evoking emotion. The bio-signals of heart and respiration were measured according to sound stimuli. HRV (heart rate variability) and BRV (breathing rate variability) spectrum were obtained from spectrum analysis of ECG (electrocardiogram) and RSP (respiration). The synchronization of HRV and BRV spectrum was analyzed according to each emotion. Statistical significance of relationship between them was tested by one-way ANOVA. There were significant relation of synchronization between HRV and BRV spectrum (synchronization of HF: F(3, 68) = 3.605, p = 0.018, ${\eta}^2_p=0.1372$, synchronization of LF: F(3, 68) = 5.075, p = 0.003, ${\eta}^2_p=0.1823$). HF difference of synchronization between ECG and RSP has been able to classify arousal from relaxation (p = 0.008, d = 1.4274) and LF's has negative from positive (p = 0.002, d = 1.7377). Therefore, it was confirmed that the heart and respiration to recognize the dimensional emotion by connectivity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1340-1349
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• 2012

Increasing the oxidation of fat through exercise is the recommendable method for weight control. Preceding researches have proposed increase in the usage of fat during exercise in stabilized state and under maximum exertion through aerobic training. However, such researches require additional equipment for gas analysis in order to measure the caloric value or gas exchange of subjects during exercise. Such equipments become highly restrictive for those exercise and cause substantially higher cost. According to this, we have presented the method of estimating the maximal fat oxidation point through changes in LF & HF which reflects changes in heart rate and the autonomic nervous system in order to induce exercise for a less restrictive and efficient fat oxidation than existing methods. We have conducted exercise stress test on subject with similar exercise abilities, and have detected the changes in heart rate and changes in LF & HF by measuring changes in fat oxidation and measuring ECG signals at the same time through a gas analyzer. Changes in heart rate and HRV of the subjects during exercising was detected through only the electrocardiographic signals from exercising and detected the point of maximum fat oxidation that differs from person to person. The experiment was carried out 16 healthy males, and used Modified Bruce Protocol, which is one of the methods of exercise stress test methods that use treadmill. The fat oxidation amount during exercise of all the subjects showed fat oxidation of more than 4Fkcal/min in the exercise intensity from about 5 minutes to 10 minutes. The correlation between the maximal fat oxidation point obtained through gas analysis and the point when 60% starts to be relevant in the range from -0.01 to 0.01 seconds for values of R-R interval from changes in heart rate had correlation coefficients of 0.855 in Kendall's method and in Spearman's rho, it showed significant results of it being p<0.01 with 0.950, respectively. Furthermore, in the changes in LF & HF, we have determined the point where the normalized area value starts to become the same as the maximal fat oxidation point, and the correlation here showed 0.620 in Kendall and 0.780 in Spearma of which both showed significant results as p<0.01.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.275-283
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• 2001

This study was performed to extract and analyze the biosignals to find the relationship between the level of anesthesia and the variations of physiological parameters during epidural anesthesia. Seven male and twenty female patients(ages from 45 to 70 years old) were participated for the experiment, and ECGs, PPGs, SKTs, SCRs were obtained during anesthesia. As results, the HF/LF ratios of HRV were decreased after the injection anesthetics. For skin temperatures, values measured from the palm was reduced and the temperatures from four channels, measured from armpit through the right side of the body, were increased. SCRs were decreased for all channels after the injection of anesthetics. However the heart rate and PPGs showed no significant changes. It was concluded that the injection of anesthetics result the changes in biosignals, and it could be explained by the degree of the sympathetic and/or parasympathetic nerve activities. Results of this study could provide the valuable information for the estimation of level for the spinal and general anesthesia, and could be extended to the development of a system which could quantify the level of anesthesia.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.134-141
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• 2017

The purpose of this study was to analyze physiological changes via a cold air reaction experiment to generate basic data that are useful for the development of an automobile active air conditioning system to prevent drowsiness. The $CO_2$ concentration causing drowsiness in vehicle operation was kept below a certain level. Air was blown to the driver's face by using an indoor air cooling apparatus. Sleepiness and the arousal state of the driver in cold wind were measured by physiological signals. It was evident in the EEG that alpha waves decreased and beta waves increased, caused by cold air stimulation. The ${\alpha}/{\beta}$ ratio was reduced by about 52.9% and an alert state confirmed. In the electrocardiogram analysis, the efficiency of cold air stimulation was confirmed by the mean heart rate interval change. The R-R interval had a delay time of about one minute compared to the EEG response. The findings confirmed an arousal effect from sleepiness due to cold air stimulation.

• Science of Emotion and Sensibility
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• v.2 no.2
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• pp.37-42
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• 1999

Long-term exposure to loud noise affects performance since it changes arousal level, distracts attention, and also is able to evoke subjective stress accompanied by negative emotional states. The purpose of the study was to analyze dynamics of subjective and physiological variables during a relatively long-lasting (30 min) exposure to white noise (85 dB[A]). Physiological signals were recorded on 15 college students during 30 min of intense auditory stimulation. Autonomic variables, namely skin conductance level , non-specific SCR number, inter-best intervals in ECG, heart rate variability index (HF/LF ratio of HRV), skin temperature, as well as respiration rate were analyzed on 5 min epoch basis. Psychological assessment (subjective rating of stress level) was also repeated every 5 min. Statistical analysis was employed to trace the time course of the dynamics of subjective and autonomic physiological variables and their relationships. Results showed that the intense noise evoked subjective stress as well as associated autonomic nervous system responses. However it was shown that physiological variables endured specific changes in the process of exposure to the loud white noise. Discussed were probable psychophysiological mechanisms mediating reactivity to long-term auditory stimulation of high intensity, namely short-term activation, followed by transient adaptation (with relatively stable autonomic balance) and then a subsequent wave of arousal due to tonic sympathetic dominance.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.411-414
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• 1997

This paper describes the design guideline, methodology and general specification of the developed 12 channel interpretive electrocardiograph. The developed 12 channel electrocardiograph consists of main module, patient module, DSP module, interface module, power/battery module, TFT color LCD and thermal recorder. The control panel of the system has full keyboard, rotate/push button, function key and unctional indicators. The graphic user interface program conveniently allows user to record, setup, store, manage ECGs. A variety of system configurations give it ability to make user favorable environment. This system also has a resting adult's ECG analysis program. The developed system and program will be continusely evolved using a database of clinically correlated ECGs.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.37-46
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• 2015

The wireless electrocardiographic monitoring system(WDMS) is designed to be long term monitoring for the early detection of cardiac disorders. The current version of the WDMS can identify two types of cardiac rhythms in real-time, such as atrial fibrillation(AF) and normal sinus rhythm(NSR), which are very important to track cardiac-rhythm disorders. In this study, we proposed the analysis method to discriminate the characteristics statistically evaluated in both time and frequency domains between AF and NSR using various parameters in the heart rate variability(HRV). And we applied various ECG detection methods (e.g., difference operation method) and compared the results with those of the discrete wavelet transform(DWT) method. From the statistically results, we found that the parameters such as STD RR, STD HR, RMSSD, NN50, pNN50, RR Trian, and TNN(p<0.05) are significantly different between the AF and NSR patients in time domain. On the other hand, the frequency domain analysis results showed a significant difference in VLF power($ms^2$), LF power($ms^2$), HF power($ms^2$), VLF(%), LF(%), and HF(%). In particular, the parameters such as STD RR, RMSSD, NN50, pNN50, VLF power, LF power and HF power were considered as the most useful parameters in both AF and NSR patient groups. Our proposed method can be efficiently applied to early detection of abnormal conditions and prevent the such abnormals from becoming serious.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.4865-4885
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• 2019

We propose a FPGA based design that performs real-time power-efficient analysis of heterogeneous sensor data using adaptive ANN on edge gateway of smart military wearables. In this work, four independent ANN classifiers are developed with optimum topologies. Out of which human activity, BP and toxic gas classifier are multiclass and ECG classifier is binary. These classifiers are later integrated into a single adaptive ANN hardware with a select line(s) that switches the hardware architecture as per the sensor type. Five versions of adaptive ANN with different precisions have been synthesized into IP cores. These IP cores are implemented and tested on Xilinx Artix-7 FPGA using Microblaze test system and LabVIEW based sensor simulators. The hardware analysis shows that the adaptive ANN even with 8-bit precision is the most efficient IP core in terms of hardware resource utilization and power consumption without compromising much on classification accuracy. This IP core requires only 31 microseconds for classification by consuming only 12 milliwatts of power. The proposed adaptive ANN design saves 61% to 97% of different FPGA resources and 44% of power as compared with the independent implementations. In addition, 96.87% to 98.75% of data throughput reduction is achieved by this edge gateway.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.287-292
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• 2003

Even though MCG has many advantages over ECG, MCG signa)s are easily corrupted by external magnetic noises Since multi-channel MCG signals are recorded simultaneously at many spatial positions, it is effective to apply spatial fitters as well as the conventional temporal filters to remove external magnetic noises. The spatial filters can be designed by utilizing the fact that the noise signals caused by external noise sources are more spatially correlated than the original MCG signals. In this paper, we introduce a spatial filtering method for the noise reduction in MCG based on the principal component analysis. Healthy volunteer study results obtained with a 61-channel MCG system are presented.