• Clinical and Experimental Pediatrics
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• 제49권4호
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• pp.341-353
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• 2006

Learning disorders are diagnosed when the individual's achievement on standardized tests in reading, mathematics, or written expression is substantially below that expected for age, schooling, and level of intelligence. Subtypes of learning disorders may be classified into two groups, language-based type learning disorders including reading and writing disorder, and nonverbal type learning disorder (NLD) such as those relating to mathematics & visuospatial skills, and those in the autism spectrum. Converging evidence indicates that reading disorder represents a disorder within the language system and more specifically within a particular subcomponent of that system, phonological processing. Recent advances in neuroimaging technology, particularly the development of fMRI, provide evidences of a neurobiological basis for reading disorder, specifically a disruption of two left hemisphere posterior brain systems, one parieto-temporal, the other occipito-temporal. The former is the reading system for beginner reading, the latter for skilled reading. Compensatory engagement of anterior systems around the inferior frontal gyrus(Broca's area) and a posterior(right occipito-temporal) system is noted in persistent poor readers in long-term follow up study. The theoretical model proposed to explain NLD's source is not right hemisphere damage, but rather the white matter model. The working hypothesis of the white matter model is that the underdevelopment of, damage to, or dysfunction of cerebral white matter(long myelinated fibers) is the source of this disorder. The role of an evidence-based effective intervention in the remediation of children with learning disorder is discussed.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제35권2호
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• pp.143-149
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• 2024

Objectives: This study aimed to identify the effectiveness of treatment programs for children with reading (RD) or mathematics disorders (MD). Structured treatment programs were developed to improve phonological awareness and number sense among children and adolescents with RD or MD, respectively, and the effectiveness of the learning disorder treatment programs were evaluated. Methods: We used standardized, objective diagnostic, and evaluation tools not only to recruit participants with RD, MD, or comorbid attention deficit and hyperactivity disorder, but also to assess the effectiveness of the treatments regarding both improved core neurocognitive deficits of RD or MD and academic achievement. Forty children with RD or MD received one-on-one treatments from therapists. Results: In the RD group, treatment effects were observed in all subtests. In the word and paragraph reading tests, the accuracy rates and fluency improved. The results of the phonological working memory test, word-sound correspondence test, and rapid automatic naming tests also improved. In the MD group, the accuracy rate and fluency on the arithmetic test improved. An increase in the accuracy rate in the size and distance comparison tests and a decrease in the error rate in the estimation test were also observed. However, there were no improvements in reaction time in these subtests. Conclusion: Learning disorder treatment programs that focus on improving phonological awareness or number sense in children with RD or MD improved achievement, phonological awareness, and number sense.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.157-162
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• 2022

Cerebral palsy is one of the most serious forms of disorders of the psychophysical development of children, which manifests itself in disturbances of motor functions, which are often combined with speech disorders, other complications of the formation of higher mental functions, and often with a decrease in intelligence. The article will discuss the speech disorder in children with cerebral palsy. Emphasis is placed on some important aspects, which should bear in mind, investigating the problem of specifics of speech development of children with cerebral palsy. In particular at the heart of speech disorders in the cerebral palsy is not only damage to certain structures of the brain, but also the later formation or underdevelopment of those parts of the cerebral cortex, which are of major importance in linguistic and mental activity. This is an ontogenetically young region of the cerebral cortex, which is most rapidly developing after birth (premotor, frontal, temmono-temporal). It is important to take into account, that children with cerebral palsy have disturbances of phonemic perception. Often, children do not distinguish between hearing sounds, cannot repeat component rows, allocate sounds in words. At dysarthria, there are violations of pronunciation of vowel and consonant sounds, tempo of speech, modulation of voice, breathing, phonation, as well as asynchronous breathing, alignment and articulation. As a result, we identified the main features and specifics of the speech development of children with cerebral palsy and described the conditions necessary for the full development of language. Language disturbances in children's cerebral palsy depend on the localization and severity of brain damage. Great importance in the mechanism of speech disorders has a pathology that limits the ability of movement and knowledge of the world.

• Nonlinear Functional Analysis and Applications
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• 제28권2호
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• pp.473-495
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• 2023

The use of mathematical modelling in the study of epidemiological disorders continues to grow substantially. In order to better support global policy initiatives and explain the possible consequence of an outbreak, mathematical models were constructed to forecast how epidemic illnesses spread. In this paper, fractional derivatives and (${\varpi}$ - F_𝓒)-contractions are used to explore the existence and uniqueness solutions of the novel coronavirus-19 model.

• 대한치매학회지
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• 제17권3호
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• pp.83-89
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• 2018

Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron. The purpose of this review is to explain the fundamental concepts of artificial neural networks.

• Molecular & Cellular Toxicology
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• 제3권2호
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• pp.77-84
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• 2007

Millions of individuals worldwide are currently afflicted with neurodegenerative disorders such as Parkinson's disease and multiple sclerosis which are caused by the death of specific types of specialized cells in the Central Nervous System (CNS). Recently, Neural Stem Cells (NSCs) are able to replace these dead cells with new functional cells, thereby providing a cure for devastating neural diseases. The clinical use of neural stem cells (NSCs) for the treatment of neurological diseases requires overcoming the scarcity of the initial in vivo NSC population. Thus, we developed a novel 3-dimentional cellular automata model for optimum production of neural stem cells and their derivatives in large scale to treat neurodegenerative disorder patients.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제8권1호
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• pp.101-112
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• 1997

본 연구의 목적은 학습 장애를 진단하는데 있어 성취도 검사인 기초학습기능검사의 유용성을 살펴보는 것이다. 학습 장애 집단은 두 유형으로 나누어 언어적 학습 장애 집단(VLD：Verbal Learning Disorder) 34명, 비언어적 학습 장애 집단(NVLD：Nonverbal Learning Disorder) 14명으로 총 48명으로 구성되었으며, 비교 집단으로는 Dysthymia 집단 11명, 정상아 20명을 대상으로 지능 검사 및 기초학습 기능검사의 수행을 비교하였다. 지능 검사에서 VLD집단은 어휘력 및 언어를 통한 학습 과제, 언어-청각적인 주의과제에서 의미있는 저하를 나타내었고, NVLD 집단은 시-지각의 정확도, 정신-운동성 기능의 협응 속도, 시각-공각적인 조직력 등 동작성 기능 전반에 걸쳐 비효율성을 나타내었다. 기초학습기능검사에서는 VLD 집단은 음운 부호화과제, 셈하기 능력, 단어 재인 과제에서 의미있는 저하를 나타내었다. 또한 지능 검사의 소검사들에 기초학습기능검사의 소검사들을 포함하여 판별 분석을 한 결과, 기초학습기능검사를 포함시키지 않은 경우보다 판별율이 높아졌을 뿐 아니라, VLD집단을 유의미하게 판별해 주는 판별 함수를 도출하였다. 각 소검사들의 속성을 분석하기 위하여 요인 분석을 실시하였으며 이를 통하여 소검사들을 유목화하였으며 마지막으로 현 논문의 제한점 및 기초학습기능검사의 제한점을 논의하였다.

• International journal of advanced smart convergence
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• 제12권3호
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• 한국정보통신학회논문지
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• 제26권9호
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• pp.1279-1292
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• 2022

신체적 불편함으로 인해 기존의 마우스와 같은 입력 장치의 사용이 힘든 사용자에게 도움이 될 수 있는 시스템으로 얼굴 인식과 Pyautogui 마우스 제어 기반의 비접촉식 입력 기법을 제안한다. 본 연구는 특히 화면 확대/축소나 스크롤 기능과 같이 웹 서핑을 보다 편리하게 돕는 기능이 포함되어 있으며, 개인의 신체적 차이 및 웹 사용 습관을 고려해 여러 설정값을 조정할 수 있도록 하였다. 또한, 기존의 시스템에서 한계점으로 제시되었던 눈 피로도에 대한 문제도 해결하였다. 추가로 고성능 CPU나 GPU 환경이 요구되지 않고 별도의 트래커 장치나 고성능 카메라 또한 필요하지 않다. 이러한 연구를 통해 손을 쓰기 어려운 장애인 및 노인들의 웹 접근성을 높여 배리어프리 실현에 기여하고자 한다.

• 대한조현병학회지
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• 제22권2호
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• pp.34-41
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• 2019

Objectives: This study was performed to examine the effects of the public attention to 'crime of schizophrenia' on the use of mental health services in patients with schizophrenia using big data analysis. Methods: Data on the frequency of internet searches for 'crime of schizophrenia' and the patterns of mental health service utilization by patients with schizophrenia spectrum disorders by month were collected from Naver big data and the Health Insurance Review and Assessment Services in Korea, respectively. Their correlations in the same and following month for lagged effect were examined. Results: The number of outpatients correlated negatively with public attention to 'crime of schizophrenia' in the same month. The lagged relationship between public attention and the number of admissions in psychiatric wards was also found. In terms of sex differences, the use of outpatient services among female patients correlated negatively with public attention in the same month while the number of male patients' admissions in both same and following month correlated positively with public attention. Conclusion: These findings suggested that public attention to 'crime of schizophrenia' could negatively affect illness behavior in patients with schizophrenia.