• 한국멀티미디어학회논문지
• /
• 제24권10호
• /
• pp.1336-1345
• /
• 2021

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neurological disorders in children. The diagnosis of ADHD in children is based on the interviews and observation reports of parents or teachers who have stayed with them. Since this approach cannot avoid long observation time and the bias of observers, another approach based on Electroencephalography(EEG) is emerging. The goal of this study is to develop an assistive tool for diagnosing ADHD by EEG classification. This study explores the frequency bands of EEG and extracts the implied features in them by using the proposed CNN. The CNN architecture has three Convolution-MaxPooling blocks and two fully connected layers. As a result of the experiment, the 30-60 Hz gamma band showed dominant characteristics in identifying EEG, and when other frequency bands were added to the gamma band, the EEG classification performance was improved. They also show that the proposed CNN is effective in detecting ADHD in children.

• International Journal of Computer Science & Network Security
• /
• 제23권4호
• /
• pp.179-186
• /
• 2023

For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Naïve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.

• 생물정신의학
• /
• 제27권1호
• /
• pp.18-26
• /
• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

• Journal of Computing Science and Engineering
• /
• 제10권4호
• /
• pp.111-117
• /
• 2016

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

• 산업경영시스템학회지
• /
• 제32권4호
• /
• pp.215-222
• /
• 2009

Human voice reacts very sensitively to human's minute physical condition. For instance, human voice disorders affect patients profoundly especially in the case of Parkinson's disease. Acoustic tools such as MDVP, can function as an equipment that measures various voice in different objects. Many different approaches have been applied for analyzing the voice disorders for diagnosis of Parkinson's disease. According to the voice data of suspected Parkinson's patients from UCI Machine Learning Repository, it is reported to have 23 people with Parkinson's disease and 8 healthy people. Applying Mahalanobis Taguchi System (MTS) for diagnosis of Parkinson's disease, the correct diagnosis performance is compared to previous research results.

• 대한한방소아과학회지
• /
• 제15권2호
• /
• pp.141-165
• /
• 2001

Attention-deficit/hyperactivity disorder(ADHD) is one of the most common childhood-onset psychlatric disorders. It is distinguished by symptoms of inattention, hyperactivity, and impulsivity. ADHD may be accompanied by learning disabilities, depression, anxiety, conduct disorder, and oppositional defiant disorder. The etiology of ADHD is unknown, and the disorder may have several different causes. Individual with ADHD present in childhood and may continue to show symptoms as they enter adolescence and adult life. Public interest in ADHD has increased along with debate in the media concerning the diagnostic process and treatment strategies. The purpose of this study is oriental medical approach to ADHD. This study was progressed for oriental diagnosis and treatment for ADHD. In oriental medicine, the reason of ADHD was deficiency of the kidney, hyperactivity of the liver(腎虛肝亢), deficiency of the heart and the spleen(心脾不足), heart disturbed by phlegm and heat(痰熱擾心). The method of medical treatment was nourishing the kidney and checking exuberance of yang(滋腎潛陽), relieving mental stress and promoting wisdom(寧神益智), nourishing the heart and strengthening the spleen(養心健脾), tranquilzation(安神定志). removing heat-phlegm(淸熱化痰), inducing resuscitation and tranquilzation(開窮安神). The prescription was commonly used as Liuwei Dihuang Wan jiajian(六味地黃丸加減), Guipi Tang he Ganmai Dazao Tang jiajian(歸脾湯合甘麥大棗湯加減), Huanglian Wendan Tang jiawei(溫黃連溫膽湯加味). It should help primary care providers in their assessment of a common child health problem.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• 제22권1호
• /
• pp.16-24
• /
• 2011

Objectives：This study examined the prevalence of psychiatric problems in children with scholastic difficulties who had been referred for mental health services from the Office of Education in Ulsan Metropolitan City. Methods：Child psychiatrists evaluated the referred children using the DSM-IV. Evaluation tools included the Wechsler Intelligence Scale for Children-III, the Children's Depression Inventory, the Korean form of the State-trait anxiety Inventory for children, the ADHD rating. Results：Seventy-six children consisting of 64 boys (84.2%) and 12 girls (15.8%) participated in the study. The average age was 10.3 (SD=0.93) years old. Approximately 74% of the children referred for scholastic difficulties were diagnosed with mental retardation. The Axis I diagnosis among these children were ADHD (86.8%), depression (21.1%), learning disorder (9.2%), communication disorder (4.8%), pervasive developmental disorder (3.6%), internet addiction (1.3%), and mood disorder (1.3%). Their overall measure according to the Child Depression Inventory was 22.7 (SD=16.8), that for the State-Trait Anxiety Inventory for Children was 33.3 (SD=7.9)/32.4 (SD=9.5), and that for the ADHD rating scale was 18.9 (SD=10.9). Conclusion：These results suggest that many children with scholastic difficulties have both complex psychiatric and educational problems.

• Journal of Korean Dental Science
• /
• 제16권1호
• /
• pp.47-62
• /
• 2023

Purpose: We aimed to investigate the objective cutoff values of unstimulated flow rates (UFR) and stimulated salivary flow rates (SFR) in patients with xerostomia and to present an optimal machine learning model with a classification and regression tree (CART) for all ages. Materials and Methods: A total of 829 patients with oral diseases were enrolled (591 females; mean age, 59.29±16.40 years; 8~95 years old), 199 patients with xerostomia and 630 patients without xerostomia. Salivary and clinical characteristics were collected and analyzed. Result: Patients with xerostomia had significantly lower levels of UFR (0.29±0.22 vs. 0.41±0.24 ml/min) and SFR (1.12±0.55 vs. 1.39±0.94 ml/min) (P<0.001), respectively, compared to those with non-xerostomia. The presence of xerostomia had a significantly negative correlation with UFR (r=-0.603, P=0.002) and SFR (r=-0.301, P=0.017). In the diagnosis of xerostomia based on the CART algorithm, the presence of stomatitis, candidiasis, halitosis, psychiatric disorder, and hyperlipidemia were significant predictors for xerostomia, and the cutoff ranges for xerostomia for UFR and SFR were 0.03~0.18 ml/min and 0.85~1.6 ml/min, respectively. Conclusion: Xerostomia was correlated with decreases in UFR and SFR, and their cutoff values varied depending on the patient's underlying oral and systemic conditions.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• 제7권2호
• /
• pp.247-257
• /
• 1996

본 연구는 삼성의료원 소아정신과 학습장애 특수 클리닉에 내원한 학습부진 아동을 대상으로 학습부진의 원인이 되는 장애와 학습부진 아동에서 학습장애의 유병율을 알아보고자 하였다 이들은 $6{\sim}15$세 사이의 197명으로 구성되었으며 결과는 아래와 같았다. 1) 대상군중 우울증등의 정서장애가 33%로 가장 많았으며 주의력결핍 과잉황동장애가 31%로 두번째의 빈도를 나타내었다. 2) 대상군중 학습장애 환아는 41명으로 20.8%의 빈도율을 보였다. 3) 학습장애의 공존병리중 주의력결핍 과잉활동장애가 44%로 가장 높은 빈도를 나타내었다. 4) 주의력결핍 과잉활동장애가 공존하는 학습장애군과 학습장애만 있는 군에서는 성별이나 연령차이, 지능차이는 없었으며 뇌파의 이상 소견에 대해서도 차이가 없었다. 그러나 주의력결핍 과잉활동장애가 없는 단독 학습장애군은 주의력결핍 과잉활동장애가 공존하는 학습장애군보다 더 늦은 나이에 발병하였고 학업성취도 면에서 우수하였는데 특히 국어, 수학, 사회, 음악 과목에서 격차가 컸다.

• 동의신경정신과학회지
• /
• 제24권4호
• /
• pp.393-402
• /
• 2013

Objectives: This study is a clinical report of a patient with ADHD and learning disorders who is being treated with hyperbaric oxygen, scalp acupuncture, cognitive enhancement therapy and speech-language therapy. Methods: The BASA-R, BASA-M and REVT tests were used for the diagnosis of learning disorders. For the treatment, hyperbaric oxygen therapy, scalp acupuncture, cognitive enhancement therapy and speech-language therapy were all being used. The Raven's matrix tests were compared for between before and after the abovementioned therapies. Results: After the treatment, Raven's matrix test grade improved from 4 to 5. The improvement of the patient's concentration, communication, motion, confidence, and sleep conditions were observed. Conclusions: These therapies including the hyperbaric oxygen therapy are efficient for the treatment of ADHD and learning disorders.