• Journal of Preventive Medicine and Public Health
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• v.31 no.4 s.63
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• pp.751-769
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• 1998

This study was conducted to assess the attitude of pregnant women toward delivery method, understanding of the reason for determining her own delivery method, participation in decision-making process and satisfaction with delivery method after labor. Study subjects were 693 pregnant women who had visited obstetric clinic for prenatal care in the last month of pregnancy in one general hospital and one obstetrics-gynecology specialty hospital in Taegu city from February 1 to March 31 in 1998. A questionnaire was administered before and after labor and a telephone interview was done one month after labor. Proportion of women who had health education and/or counselling about delivery method during prenatal care was 24.0% and this proportion was higher for women who had previous c-section(35.5%) than others. Women thought vaginal delivery is better than c-section for both maternal and baby's health regardless of previous delivery method. About 90% of primipara and multiparous women who had previous vaginal delivery wanted vaginal delivery for the index birth, while 85.6% of multiparous women who had previous c-section wanted repeat c-section. Reasons for choosing c-section in pregnant women who preferred vaginal delivery before labor were recommendation of doctors(81.9%), recommendation of husband (0.8%), agreement between doctor and pregnant woman(4.7%), and mother's demand (12.6%). Reasons for choosing vaginal delivery were mother's demand(30.6%) and no indication for c-section(67.2%). Reasons for choosing c-section in pregnant women who preferred c-section before labor were recommendation of doctors(76.2%), mother's demand(20.0%), recommendation of husband(1.3%), and agreement between doctor and pregnant woman(2.5%). Of the pregnant women who had c-section, by doctor's recommendation, the proportion of women who had heard detailed explanation about reason for c-section by doctor was 55.1%. Mother's statement about the reason for c-section was consistent with the medical record in 75.9% . However, over 5% points disparities were shown between mother's statement and medical record in cases of the repeat c-section and mother's demand. In primipara and multiparous women who had previous vaginal delivery, the delivery method for index birth had statistically significant association with the preference of delivery method before labor(p<0.05). All of the women who had previous c-section had delivered the index baby by c-section. Among mothers who had delivered the index baby vaginally, 84.9% of them were satisfied with their delivery method immediately after labor and 85.1% at 1 month after labor. However, mothers who had c-section stated that they are satisfied with c-section in 44.6% immediately after labor and 42.0% at 1 month after labor. Preferred delivery method for the next birth had statistically significant association with delivery method for the index birth both immediately after labor and in 1 month after labor. The proportion of mothers who prefer vaginal delivery for the next birth increased with the degree of satisfaction with the vaginal delivery for the index birth but the proportion of mothers who prefer c-section for the next birth was high and they did not change significantly with the degree of satisfaction with the c-section for the index birth. These results suggest that the current high technology-based, physician-centered prenatal and partritional cares need to be reoriented to the basic preventive and promotive technology-based, and mother-fetus-centered care. It is also suggested that active involvement of pregnant woman in decision-making process for the delivery method will increase the rate of vaginal birth after c-section and decrease c-section rate and improve the degree of maternal satisfaction after delivery.

• Journal of Chest Surgery
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• v.31 no.3
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• pp.233-241
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• 1998

To define the risk factors affecting the early major morbidity and mortality after multiple- valve operations, the preoperative, intraoperative and postoperative informations were retrospectively collected on 124 consecutive patients undergoing a multiple-valve operation between October 1985 and July 1996 at the department of Thoracic and Cardiovascular Surgery of Pusan Paik Hospital. The study population consists of 53 men and 71 women whose mean age was 37.9$\pm$11.5(mean$\pm$SD) years. Using the New York Heart Association(NYHA) classification, 41 patients(33.1%) were in functional class II, 60(48.4%) in class III, and 20(16.1%) in class IV preoperatively. Seven patients(5.6%) had undergone previous cardiac operations. Atrial fibrillations were present in 76 patients(61.3%), a history of cerebral embolism in 5(4.0%), and left atrial thrombus in 13(10.5%). The overall early mortality rate and postoperative morbidity was 8.1% and 21.8% respectively. Among the 124 cases of multiple-valve operation, there were 57(46.0%) of combined mitral valve replacement(MVR) and aortic valve replacement(AVR), 48(38.7%) of combined MVR and tricuspid annuloplasty(TVA), 12(9.7%) of combined MVR, AVR and TVA, 3(2.4%) of combined MVR and aortic valvuloplasty, 2(1.6%) of combined MVR and tricuspid valve replacement, and others. The patients were classified according to the postoperative outcomes; Group A(27 cases) included the patients who had early death or major morbidity such as low cardiac output syndrome, mediastinitis, cardiac rupture, ventricular arrhythmia, sepsis, and others; Group B(97 cases) included the patients who had the good postoperative outcomes. The patients were also classified into group of early death and survivor. In comparison of group A and group B, there were significant differences in aortic cross-clamping time(ACT, group A:153.4$\pm$42.4 minutes, group B:134.0$\pm$43.7 minutes, p=0.042), total bypass time(TBT, group A:187.4$\pm$65.5 minutes, group B:158.1$\pm$50.6 minutes, p=0.038), and NYHA functional class(I:33.3%, II:9.7%, III:20%, IV:50%, p=0.004). In comparison of early death(n=10) and survivor(n=114), there were significant differences in age(early death:45.2$\pm$8.7 years, survivor:37.2$\pm$11.6 years, p=0.036), sex(female:12.7%, male:1.9%, p=0.043), ACT(early death:167.1$\pm$38.4 minutes, survivor:135.7$\pm$43.7 minutes, p=0.030), and NYHA functional class(I:0%, II:4.9%, III:1.7%, IV:35%, p=0.001). In conclusion, the early major morbidity and mortality were influenced by the preoperative clinical status and therefore the earlier surgical intervention should be recommended whenever possible. Also, improved methods of myocardial protection and operative techniques may reduce the risk in patients with multiple-valve operation.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Korea Technology Innovation Society
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• v.10 no.1
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• pp.73-97
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• 2007

R&D into future technologies should be conducted in conjunction with technological innovation strategies that are linked to corporate survival within a framework of information and knowledge-based competitiveness. As such, future technology strategies should be ensured through open R&D organizations. The development of future technologies should not be conducted simply on the basis of future forecasts, but should take into account customer needs in advance and reflect them in the development of the future technologies or services. This research aims to select as segmentation variables the customers' attitude towards accepting future telecommunication technologies and their value orientation in their everyday life, as these factors wilt have the greatest effect on the demand for future telecommunication services and thus segment the future telecom service market. Likewise, such research seeks to segment the market from the stage of technology R&D activities and employ the results to formulate technology development strategies. Based on the customer attitude towards accepting new technologies, two groups were induced, and a hierarchical customer segmentation model was provided to conduct secondary segmentation of the two groups on the basis of their respective customer value orientation. A survey was conducted in June 2006 on 800 consumers aged 15 to 69, residing in Seoul and five other major South Korean cities, through one-on-one interviews. The samples were divided into two sub-groups according to their level of acceptance of new technology; a sub-group demonstrating a high level of technology acceptance (39.4%) and another sub-group with a comparatively lower level of technology acceptance (60.6%). These two sub-groups were further divided each into 5 smaller sub-groups (10 total smaller sub-groups) through two rounds of segmentation. The ten sub-groups were then analyzed in their detailed characteristics, including general demographic characteristics, usage patterns in existing telecom services such as mobile service, broadband internet and wireless internet and the status of ownership of a computing or information device and the desire or intention to purchase one. Through these steps, we were able to statistically prove that each of these 10 sub-groups responded to telecom services as independent markets. We found that each segmented group responds as an independent individual market. Through correspondence analysis, the target segmentation groups were positioned in such a way as to facilitate the entry of future telecommunication services into the market, as well as their diffusion and transferability.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.5
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• pp.853-860
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• 2012

To estimate potential use of fly ash in reducing $CH_4$ and $CO_2$ emission from soil, $CH_4$ and $CO_2$ fluxes from a paddy soil mixed with fly ash at different rate (w/w; 0, 5, and 10%) in the presence and absence of fertilizer N ($(NH_4)_2SO_4$) addition were investigated in a laboratory incubation for 60 days under changing water regime from wetting to drying via transition. The mean $CH_4$ flux during the entire incubation period ranged from 0.59 to $1.68mg\;CH_4\;m^{-2}day^{-1}$ with a lower rate in the soil treated with N fertilizer due to suppression of $CH_4$ production by $SO_4^{2-}$ that acts as an electron acceptor, leading to decreases in electron availability for methanogen. Fly ash application reduced $CH_4$ flux by 37.5 and 33.0% in soils without and with N addition, respectively, probably due to retardation of $CH_4$ diffusion through soil pores by addition of fine-textured fly ash. In addition, as fly ash has a potential for $CO_2$ removal via carbonation (formation of carbonate precipitates) that decreases $CO_2$ availability that is a substrate for $CO_2$ reduction reaction (one of $CH_4$ generation pathways) is likely to be another mechanisms of $CH_4$ flux reduction by fly ash. Meanwhile, the mean $CO_2$ flux during the entire incubation period was between 0.64 and $0.90g\;CO_2\;m^{-2}day^{-1}$, and that of N treated soil was lower than that without N addition. Because N addition is likely to increase soil respiration, it is not straightforward to explain the results. However, it may be possible that our experiment did not account for the substantial amount of $CO_2$ produced by heterotrophs that were activated by N addition in earlier period than the measurement was initiated. Fly ash application also lowered $CO_2$ flux by up to 20% in the soil mixed with fly ash at 10% through $CO_2$ removal by the carbonation. At the whole picture, fly ash application at 10% decreased global warming potential of emitted $CH_4$ and $CO_2$ by about 20%. Therefore, our results suggest that fly ash application can be a soil management practice to reduce green house gas emission from paddy soils. Further studies under field conditions with rice cultivation are necessary to verify our findings.

• Journal of Technology Innovation
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• v.17 no.1
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• pp.1-48
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• 2009

Recently we have found some symptoms that R&D fiscal incentives might not work well what it has intended through the analysis of current statistics of firm's R&D data. Firstly, we found that the growth rate of R&D investment in private sector during the recent decade has been slowdown. The average of growth rate (real value) of R&D investment is 7.1% from 1998 to 2005, while it was 13.9% from 1980 to 1997. Secondly, the relative share of R&D investment of SME has been decreased to 21%('05) from 29%('01), even though the tax credit for SME has been more beneficial than large size firm, Thirdly, The R&D expenditure of large size firms (besides 3 leading firms) has not been increased since late of 1990s. We need to find some evidence whether fiscal incentives are effective in increasing firm's R&D investment. To analyse econometric model we use firm level unbalanced panel data for 4 years (from 2002 to 2005) derived from MOST database compiled from the annual survey, "Report on the Survey of Research and Development in Science and Technology". Also we use fixed effect model (Hausman test results accept fixed effect model with 1% of significant level) and estimate the model for all firms, large firms and SME respectively. We have following results from the analysis of econometric model. For large firm: i ) R&D investment responds elastically (1.20) to sales volume. ii) government R&D subsidy induces R&D investment (0.03) not so effectively. iii) Tax price elasticity is almost unity (-0.99). iv) For large firm tax incentive is more effective than R&D subsidy For SME: i ) Sales volume increase R&D investment of SME (0.043) not so effectively. ii ) government R&D subsidy is crowding out R&D investment of SME not seriously (-0.0079) iii) Tax price elasticity is very inelastic (-0.054) To compare with other studies, Koga(2003) has a similar result of tax price elasticity for Japanese firm (-1.0036), Hall((l992) has a unit tax price elasticity, Bloom et al. (2002) has $-0.354{\sim}-0.124$ in the short run. From the results of our analysis we recommend that government R&D subsidy has to focus on such an areas like basic research and public sector (defense, energy, health etc.) not overlapped private R&D sector. For SME government has to focus on establishing R&D infrastructure. To promote tax incentive policy, we need to strengthen the tax incentive scheme for large size firm's R&D investment. We recommend tax credit for large size film be extended to total volume of R&D investment.

• The Journal of the Petrological Society of Korea
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• v.21 no.2
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• pp.129-150
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• 2012

The tectonic evolution of the Central Ogcheon Belt has been newly analyzed in this paper from the detailed geological maps by lithofacies classification, the development processes of geological structures, microstructures, and the time-relationship between deformation and metamorphism in the Ogcheon, Cheongsan, Mungyeong Buunnyeong, Busan areas, Korea and the fossil and radiometric age data of the Ogcheon Supergroup(OSG). The 1st tectonic phase($D^*$) is marked by the rifting of the original Gyeonggi Massif into North Gyeonggi Massif(present Gyeonggi Massif) and South Gyeonggi Massif (Bakdallyeong and Busan gneiss complexes). The Joseon Supergroup(JSG) and the lower unit(quartzose psammitic, pelitic, calcareous and basic rocks) of OSG were deposited in the Ogcheon rift basin during Early Paleozoic time, and the Pyeongan Supergroup(PSG) and its upper unit(conglomerate and pelitic rocks and acidic rocks) appeared in Late Paleozoic time. The 2nd tectonic phase(Ogcheon-Cheongsan phase/Songnim orogeny: D1), which occurred during Late Permian-Middle Triassic age, is characterized by the closing of Ogcheon rift basin(= the coupling of the North and South Gyeonggi Massifs) in the earlier phase(Ogcheon subphase: D1a), and by the coupling of South China block(Gyeonggi Massif and Ogcheon Zone) and North China block(Yeongnam Massif and Taebaksan Zone) in the later phase(Cheongsan subphase: D1b). At the earlier stage of D1a occurred the M1 medium-pressure type metamorphism of OSG related to the growth of coarse biotites, garnets, staurolites. At its later stage, the medium-pressure type metamorphic rocks were exhumed as some nappes with SE-vergence, and the giant-scale sheath fold, regional foliation, stretching lineation were formed in the OSG. At the D1b subphase which occurs under (N)NE-(S)SW compression, the thrusts with NNE- or/and SSW-vergence were formed in the front and rear parts of couple, and the NNE-trending Cheongsan shear zone of dextral strike-slip and the NNE-trending upright folds of the JSG and PSG were also formed in its flank part, and Daedong basin was built in Korean Peninsula. After that, Daedong Group(DG) of the Late Triassic-Early Jurassic was deposited. The 3rd tectonic phase(Honam phase/Daebo orogeny: D2) occurred by the transpression tectonics of NNE-trending Honam dextral strike-slip shearing in Early~Late Jurassic time, and formed the asymmetric crenulated fold in the OSG and the NNE-trending recumbent folds in the JSG and PSG and the thrust faults with ESE-vergence in which pre-Late Triassic Supergroups override DG. The M2 contact metamorphism of andalusite-sillimanite type by the intrusion of Daebo granitoids occurred at the D2 intertectonic phase of Middle Jurassic age. The 4th tectonic phase(Cheongmari phase: D3) occurred under the N-S compression at Early Cretaceous time, and formed the pull-apart Cretaceous sedimentary basins accompanying the NNE-trending sinistral strike-slip shearing. The M3 retrograde metamorphism of OSG associated with the crystallization of chlorite porphyroblasts mainly occurred after the D2. After the D3, the sinistral displacement(Geumgang phase: D4) occurred along the Geumgang fault accompanied with the giant-scale Geumgang drag fold with its parasitic kink folds in the Ogcheon area. These folds are intruded by acidic dykes of Late Cretaceous age.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.8 no.1
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• pp.92-100
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• 1997

Objective：The objective of this study is to examine the clinical characteristics and behavioral comorbidity of patients with Tourette’s disorder. Method：Subjects consisted of 157 patients with Tourette’ disorder diagnosed by DSM-IIIR, who were examined and diagnosed from Jan. 1988 to May 1994 at the Tourette’s Clinic of Yonsei University Medical Center. Characteristics and behavioral comorbidity of Patients were assessed by a semi-structured interview schedule. Behavioral problems like hyperactivity, obsession-compulsion, self destructiveness, enuresis, sleep problem were assessed by global clinical impression. Results：The mean age of patients was 14.49(${\pm}7.99$) years. Patients consisted of 138 males (87.9%) and 19 females(12.1%). The sex ratio was 7：1, showing a male preponderance. The number of right-handers was 133(84.7%), and the number of non-right handers was 24(15.3%). Mean age of onset was 8.85(${\pm}4.56$) years, ranging from 2-to-16 years. More than half of the patients had their age of onset at 6-10 years. Bimodal peak in age of onset was observed；the first peak was around 6 and the second peak was around 10 years. There was no sex difference in bimodal age of onset. The most common initial symptom was eye blinking. More than 55% of patients reported eye blinking as their first symptom. The second common initial symptom was head turning and the third was vocal tic. The most common symptoms that patients reported on their first visit since onset were eye blinking(82.2%), head turning or nodding(57.9%), shoulder shrugging(52.7%) and forearm movement(32.6%). Of 157 cases, 101(64.3%) patients showed downward progression of symptoms, and 25(15.9%) showed upward progression of symptoms. Nineteen fathers(12%) of patients had a past history of obsessive-compulsive disorder(OCD). Seventeen fathers(10.6%) had a history of tic disorder. SSevenmothers(4.5%) had OCD, 4 mothers (2.5%) had tic disorder. One hundred and eighteen patients(75.1%) had comorbid hyperactivity, 95 patients(60.5%) had obsession, 55 patiens(35.0%) had self destructiveness, 46 patients(29.3%) had impulsivity, and 35 patients(22.3%) had enuresis. Age of onset had a significant positive correlation with age, duration, and the global severity of obsession；and a negative correlation with the severity of hyperactivity. Hyperactivity had a significant positive correlation with impulsivity, obsession-compulsion, enuresis, and self destructiveness. Obsession-compulsion had a significant positive correlation with hyperactivity, sleep problems, and self destructiveness. Conclusion：These data suggest that clinical characteristcs and behavioral comorbidity of patients with Tourette’ disorder in this study are similar to previous research findings in Korea and other contries. The younger the age of onset was, the more severe hyperactivity was, and the less severe obsession-compulsion was. And severity of hyperactivity had a positive correlation with the severity of obsession-compulsion, impulsivity, enuresis, and self destructiveness.

• Sleep Medicine and Psychophysiology
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• v.22 no.2
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• pp.70-76
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• 2015

Background and Objectives: Insomnia is the most prevalent sleep disorder in the general population and is considered to be a disorder of hyperarousal. The aim of this study was to measure the psychophysiological responses in insomnia patients using a biofeedback system, and to compare them with results from normal healthy subjects. Materials and Methods: Eighty patients with primary insomnia (35 males and 45 females, average age $49.71{\pm}12.91years$) and 101 normal healthy controls (64 males and 37 females, average age $27.65{\pm}2.77$) participated in this study. Electromyography (EMG), heart rate (HR), skin conductance (SC), skin temperature (ST), and respiratory rate (RR) were recorded using a biofeedback system during 5 phases (baseline, stress 1, recovery 1, stress 2, recovery 2) of a stress reactivity test, and average values were calculated. Difference in values between the two groups in each corresponding phase was analyzed with independent t-test, and change in values across phases of the stress reactivity test was analyzed with paired t-test (all two-tailed, p<0.05). Results: Compared to normal controls, insomnia patients had higher EMG in all 5 phases (baseline : $7.72{\pm}3.88{\mu}V$ vs. $4.89{\pm}1.73{\mu}V$, t = -6.06, p<0.001 ; stress 1 : $10.29{\pm}5.16{\mu}V$ vs. $6.63{\pm}2.48{\mu}V$, t = -5.84, p<0.001 ; recovery 1 : $7.87{\pm}3.86{\mu}V$ vs. $5.17{\pm}2.17{\mu}V$, t = -5.61, p<0.001 ; stress 2 : $10.22{\pm}6.07{\mu}V$ vs. $6.98{\pm}2.98{\mu}V$, t = -4.37, p<0.001 ; recovery 2 : $7.88{\pm}4.25{\mu}V$ vs. $5.17{\pm}1.99{\mu}V$, t = -5.27, p<0.001). Change in heart rate across phases of the stress reactivity test were higher in normal controls than in insomnia patients (stress 1-baseline : $6.48{\pm}0.59$ vs. $3.77{\pm}0.59$, t = 3.22, p = 0.002 ; recovery 1- stress 1 : $-5.36{\pm}0.0.59$ vs. $-3.16{\pm}0.47$, t = 2.91, p = 0.004 ; stress 2-recovery 1 : $8.45{\pm}0.61$ vs. $4.03{\pm}0.47$, t = 5.72, p<0.001 ; recovery 2-stress 2 : $-8.56{\pm}0.65$ vs. $4.02{\pm}0.51$, t = -5.31, p<0.001). Conclusion: Psychophysiological profiles of insomnia patients in a stress reactivity test were different from those of normal healthy controls. These results suggest that the sympathetic nervous system is more highly activated in insomnia patients.