• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.2
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• pp.165-178
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• 2001

In order to elucidate the biological etiology and the relationship between the ontogenesis of serotonin system and psychopathology in ADHD, plasma serotonin(5-hydroxytryptamine, 5-HT) and 5-hydroxyindoleacetic acid(5-HIAA) were measured and the correlation between the plasma levels of 5-HT and 5-HIAA and age were evaluated in 46 ADHD patients and 18 control subjects. The ADHD patients were composed of 16 combined type, 10 inattentive type, and 20 hyperactive-impulsive type and the control subjects were communication disorders. The results are summarized as follows：1) There was significant difference in plasma 5-HT levels among combined, inattentive and hyperactive-impulsive and control subjects(ANOVA F=4.33, df 3, 60, p<0.05), and post-hoc test using Scheffe method showed significant difference between the combined type and control group. But, post-hoc tests showed no significant differences between combined and inattentive, combined and hyperactive-impulsive, hyperactive-impulsive and inattentive, hyperactive-impulsive and control and inattentive and control groups. 2) There was no significant differences in plasma 5-HIAA levels among the combined, hyperactive- impulsive, inattentive and control groups(ANOVA F=2.08, df 3, 60, p>0.05). 3) Significant difference in 5-HT level was found between the whole ADHD group(N=46) and the control group(N=18)(T=3.10, df 62, p<0.05). But no significant difference in 5-HIAA level was found between the whole ADHD group and the control group(T=1.90, df 62, p>0.05). 4) Plasma 5-HT and 5-HIAA levels showed no significant correlation with TOVA findings(5-HT：omission pearson correlation 0.10, commision 0.23, reaction time 0.01, variability in attention 0.11, all p>0.05, 5-HIAA：omission 0.21, commision 0.15, reaction time 0.09, variability in attention 0.15, all p>0.05). 5) Plasma 5-HT and 5-HIAA levels showed no significant correlation with attention, hyperactivity and impulsivity based on DSM-IV criteria. 6) Plasma 5-HT and 5-HIAA levels showed no significant correlation with age both in ADHD and control group. These findings show that decreased plasma 5-HT level may play a role in the genesis of ADHD, but this finding has no significant correlation with the psychopathology of ADHD. And we could not find any significant differences in ontogenetic processes in 5-HT. Future studies should be focused on the drug effects, family history and prognosis based on the biochemical subtypes(high and low 5-HT group).

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.16 no.2
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• pp.219-230
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• 2005

Objectives : This study was designed to compare the demographic data, clinical characteristics, developmental delay, and psychological tests between childhood-onset and adolescent-onset schizophrenic in-patients. Methods Medical records of the 17 childhood-onset (very early onset) Schizophrenia and 16 adolescent-onset (early onset) Schizophrenia in-patients were reviewed. Sex, age, psychiatric past history, prodromal symptoms and period, subtype, co-morbid disease, developmental delay, prescribed drug and dosage, treatment response, intelligence quotient (IQ), and Rorschach test were evaluated. Results : The mean admission age of childhood-onset (very early onset) group and adolescent-onset (early onset) group were 12.69$({\pm}2.34)$ and 15.13$({\pm}1.04)$ years. The mean onset age of childhood-onset(very early onset) group and adolescent-onset (early onset) group were 10.79$({\pm}1.95)$ and 14.46$({\pm}0.82)$ years. The mean prodromal period of childhood-onset (very early onset) group and adolescent-onset (early onset) group were 15.94$({\pm}12.33)$ and 8.06$({\pm}6.10)$ month. The time to remission period of childhood-onset (very early onset) group and adolescent-onset (early onset) group were 50.58$({\pm}24.67)$ and 30.06$({\pm}18.04)$ days. Longer time to remission period in childhood-osnet (very early onset) group was associated with earlier age of onset. The mean of total IQ, performance IQ, verbal IQ were at an average level. Discussion : Childhood-onset (very early onset) group and adolescent-onset (early onset) group Schizophrenia had different clinical and psychological features including prodromal period, and IQ subtests.

• Clinical and Experimental Pediatrics
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• v.49 no.10
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• pp.1056-1060
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• 2006

Purpose : As hearing ability affects language and cognitive development, early detection and intervention of congenital hearing defects is very important. We analyzed the result of newborn hearing screening using automated auditory brainstem response and estimated the incidence of congenital hearing defects in newborn infants in Korea. Methods : Hearing screening tests were done on 7,218 newborn infants who were delivered at Cheil General Hospital from July 1, 2004 to June 30, 2005. The first screening test was done on the second day of life with automated auditory brainstem response(AABR) using $ALGO{\bigcirc}^{(3)}$ Newborn hearing screener($Natus^{(R)}$ Medical Incorporated, San Carlos, USA) with 35 dB sound level. The newborn infants who did not pass the initial screening test took the second screening AABR test before discharge from the nursery. Infants who did not pass these screenings at the nursery were followed up at the Department of Otorhinolaryngology, Samsung Seoul Hospital. Results : Total 7,218 infants(83.3 percent of total 8,664 live births of the Cheil General Hospital) were screened in the nursery, and 55 of them failed to pass the newborn screening. Among 55 infants who were referred, six were lost during follow-up, and 14 were confirmed as hearing impaired. Six of them(42.8 percent) do not have any risk factors for hearing impairment. We can estimate that the incidence of hearing defects is about 1.9-2.8 per 1,000 live births. Conclusion : Automated auditory brainstem response is an effective tool to screen the hearing of newborn infants. Congenital hearing loss is more frequent than metabolic diseases on which screening tests are available in the newborn period. About 40 percent of infants who have hearing defects do not have any risk factors for hearing impairment. Therefore, universal newborn hearing screening must be recommended to all neonates.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.1
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• pp.103-114
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• 2001

Tourette's disorder is a disease which manifests one or more motor tics and vocal tics for more than a year. Chronic motor tic or vocal tic disorders are characterized by only one kind of tics for more than a year. We intended to investigate the clinical characteristics of the patients with chronic motor tic disorders or Tourette's disorders who had admitted from May 1, 1998 to May 1, 1999 to Seoul National University Hospital Child and Adolescent Psychiatry ward. In addition, we compared the clinical characteristics of the patients in order to elucidate the relationship between the two disorders. The patients with learning disabilities were selected as controls. There was no statistically significant difference between the onsets of the patients with chronic motor tic disorders(n=13, $7.3{\pm}2.5$ years), and Tourette's disorder(n=39, $7.2{\pm}2.2$ years), but with learning disability($4.2{\pm}1.9$ years). Also, the patients with chronic motor tic disorder and Tourette's disorder showed similar age at admission($11.7{\pm}2.7$ versus $11.5{\pm}2.6$ years), duration of admission($5.7{\pm}5.4$ versus $11.0{\pm}8.7$ weeks), mothers' ages at child birth($27.3{\pm}2.9$ versus $28.3{\pm}6.7$ years old),and fathers' age at child birth($32.2{\pm}3.2$ versus $33.3{\pm}5.2$ years old). We observed that those who had learning disabilities were alike in those aspects, except for age at visit to clinic($9.8{\pm}3.2$ years old). Family history of psychiatric illnesses(24.1% versus 46.2%), recognized precipitating factors(11.1% versus 35.7%) and response to pharmacological treatments(77.8% versus 76.9%) of the patients with chronic motor tic disorders and Tourette's disorders were observed and no differences were found. Comorbid patterns of diseases were noted. Intrafamilial conflicts were more common in the patients with learning disabilities than those with chronic tic disorders or Tourette's disorders. Precipitating factors were observed more frequent in chronic tic disorder and Tourette's disorder than learning disability. Neurocognitive profiles were investigated, and verbal IQs of the patients with chronic motor tic disorder, Tourette's disorder and learning disability were $92.3{\pm}10.7$, $94.7{\pm}14.9$, $94.3{\pm}13.8$, performance IQs $93.0{\pm}20.5$, $97.5{\pm}13.0$, $95.0{\pm}16.9$ and full-scale IQs $91.9{\pm}20.1$, $95.8{\pm}14.5$, $93.9{\pm}15.1$, respectively, which were found to be not significantly different. No difference was found in structural neurological abnormalities and EEG profiles. The patients with learning disabilities showed more common Bender-Gestalt test abnormalities. In conclusion, we have not found any affirmative clues for the division of chronic motor tic disorder and Tourette's disorder in clinical perspective.

• Korean Journal of Cognitive Science
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• v.20 no.2
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• pp.125-154
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• 2009

This paper describes the possibility of human physiological data, especially brain-wave activity, to detect cognitive overload, a phenomenon that may occur while learner uses an e-learning system. If it is found that cognitive overload to be detectable, providing appropriate feedback to learners may be possible. To illustrate the possibility, while engaging in cognitive activities, cognitive load levels were measured by EEG (electroencephalogram) to seek detection of cognitive overload. The task given to learner was a computerized listening and recall test designed to measure working memory capacity, and the test had four progressively increasing degrees of difficulty. Eight male, right-handed, university students were asked to answer 4 sets of tests and each test took from 61 seconds to 198 seconds. A correction ratio was then calculated and EEG results analyzed. The correction ratio of listening and recall tests were 84.5%, 90.6%, 62.5% and 56.3% respectively, and the degree of difficulty had statistical significance. The data highlighted learner cognitive overload on test level of 3 and 4, the higher level tests. Second, the SEF-95% value was greater on test3 and 4 than on tests 1 and 2 indicating that tests 3 and 4 imposed greater cognitive load on participants. Third, the relative power of EEG gamma wave rapidly increased on the 3rd and $4^{th}$ test, and signals from channel F3, F4, C4, F7, and F8 showed statistically significance. These five channels are surrounding the brain's Broca area, and from a brain mapping analysis it was found that F8, right-half of the brain area, was activated relative to the degree of difficulty. Lastly, cross relation analysis showed greater increasing in synchronization at test3 and $4^{th}$ at test1 and 2. From these findings, it is possible to measure brain cognitive load level and cognitive over load via brain activity, which may provide atimely feedback scheme for e-learning systems.