• Journal of Broadcast Engineering
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• v.22 no.2
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• pp.253-256
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• 2017

This paper deals with the implementation of speech disorder detection system based on machine learning classification. Problems with speech are a common early symptom of a stroke or other brain injuries. Therefore, detection of speech disorder may lead to correction and fast medical treatment of strokes or cerebrovascular accidents. The speech disorder system can be implemented by extracting features from the input speech and classifying the features using machine learning algorithms. Ten machine learning algorithms with various scaling methods were used to discriminate speech disorder from normal speech. The detection system was evaluated by the TORGO database which contains dysarthric speech collected from speakers with either cerebral palsy or amyotrophic lateral sclerosis.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.17 no.3
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• pp.77-84
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• 2017

Purpose: From the early 1990's, use of Tandem mass spectrometry in neonatal screening test, made early stage detection of disorders that was not detectable by the previous methods of inspection. This research aims to evaluate the frequency of positive results in national neonatal screening test by Tandem mass spectrometry and its usefulness. Methods: A designated organization for inherited metabolic disorder executed neonatal screening test on newborns using Tandem mass spectrometry from January 2006 to December 2015, followed by the investigation of these data by the Planned Population Federation of Korea (PPFK), and this research analyzed those inspected data from the PPFK. Results: Among total childbirth of 4,590,606, from January 2006 to December 2015, 3,445,238 were selected for MS/MS and conduction rate was 75.1%. 261 out of the selected 3,445,238 were confirmed patients and for last decade, detection rate of total metabolic disorder was 1/13,205. In 261 confirmed patients, 120 had an amino acid metabolic disorder and its detection rate was 1/28,710 and 110 had an organic acid metabolic disorder and detection rate was 1/31,320. Also, 31 had a fatty acid metabolic disorder and detection rate was 1/13,205. Conclusion: Inherited metabolic disorder is very rare. Until now, it was difficult to precisely grasp an understanding on the national incidence of inherited metabolic disorder, due to lack of overall data and inconsistent and incomplete long-term result analysis. However, this research attempted to comprehensively approach the domestic incidence, by analyzing previous 10 years of data.

• Journal of Korean Academy of Nursing
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• v.35 no.1
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• pp.5-15
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• 2005

Purpose: This study was conducted to develop a behavioral checklist to predict an autistic disorder and to identify the earliest detecting time. Method: One hundred and fifty eight children including normal, autistic, institutionalized normal, and retarded were assessed using critical interaction behavioral markers from literature review. Data was collected by semi-structured mother-child interaction by videotape recording and analyzed byfactor analysis, Cronbach a, Kappa, $x^2$, and Duncan. Result: Ten behavioral markers were sorted into 2 factors; joint-attention and synchronized behavior. Autistic children were impaired in pretend play, odeclarative pointing, proimperative pointing, gaze-monitoring, referential looking, showing, joint-attention, rhythmical vocal exchange, and synchronized laughing. The sychronized behavior was also a critical marker to predict the autistic disorder. However, it was difficult to differentiate autistic disorder from mental retardation. In addition, the appropriate detecting time was around 18 months after birth. Conclusion: This checklist should be behavior markers to predict autistic disorder and could be useful as educational material at children's clinics, parents class, and for caregivers in the health center. In addition, early detection should lead to treatment being started as soon after 18 months of age as possible.

• Journal of Korean Public Health Nursing
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• v.22 no.2
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• pp.165-176
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• 2008

Purpose: This study was conducted in order to analyze the factors that influence alcohol use disorder in elders in KyongBuk province. The results of the study will provide basic information for the development of nursing interventions to promote proper alcohol use in elders. Methods: The subjects were 626 elderly individuals. Alcohol use disorder was defined as a score of more than 15 point on the AUDIT-K(Korean version of Alcohol Use Identification Test). Descriptive statistics, chi-square test, ANOVA and logistic regression were utilized to analyze the data. Results: Overall incidence of alcohol use disorder level; normal 73.2%, problem drinking 7.5% and alcohol use disorder 19.3%. Significant factors affecting alcohol use disorder were sex (OR=6.897), religious belief (OR=1.836), smoking (OR=2.948), liver disease (OR=4.753) and depression (OR=1.779). Conclusion: Community health care nurses perform a crucial function in the screening of elderly alcoholics. Early detection and treatment of depression in elders may help to prevent alcoholism.

• Clinical and Experimental Pediatrics
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• v.51 no.9
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• pp.911-921
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• 2008

Learning disabilities (LD) are heterogeneous group of disorders with evidences of genetic or familial trait, intrinsic to the individual and presume to be due to central nervous dysfunction. Learning disabilities and attention deficit hyperactivity disorder (ADHD) are the two of the most common disorders in the population of school-age children. Typically academic achievements in children with learning disabilities are significantly lower than expected by their normal or above normal range of IQ. Although academic and cognitive deficits are hallmarks of children with LD, those children are also at risk for a broad range of behavioral and emotional problems. Almost all cases meet criteria for at least one additional diagnosis such as ADHD, developmental coordination disorder, depression, anxiety, obsessive compulsive disorder, tic disorder, among which ADHD is particularly predominant. Because of the response to the therapeutic intervention program is promising and positive when applied early, it is critical to recognize patients as early as possible. Pediatricians often are the first to hear from parents worried about a childs academic progress. It is not the responsibility of pediatrician to make a diagnosis, referring children for a diagnostic evaluation of LD is a reasonable first step. Pediatricians can make early referral of suspicious children by asking some serial short questions about basic and processing skills. With a basic knowledge about the clinical characteristics, diagnostic and therapeutic procedures of LD, pediatricians also can provide primary counseling and education for parents at their outpatient clinical settings.

• Clinical and Experimental Pediatrics
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• v.64 no.7
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• pp.339-340
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• 2021

• Journal of Oriental Neuropsychiatry
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• v.16 no.2
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• pp.287-299
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• 2005

Dissociative disorder is a psychiatric disorder characterized by a sudden loss of memory, but which has no organic disease or explanation. It usually occurs after heavy psychosocial stress or traumatic experience. A transient cerebral ischemic attack (TIA) is an acute episode of temporary and focal loss of cerebral function of vascular origin. TIAs are rapid in onset; symptoms reach their maximal manifestation in fewer than 5 minutes. Manifestations are of variable duration and typically last 2-15 minutes(rarely as long as 24 h). Most TIA durations are less than 1 hour. Of concern is the careful detection of changes in behavior, speech, gait, memory, movement, and vision. TIAs are uncommon in persons younger than 60 years. I treat 6 cases of Sudden Temporary Amnesia Patients with oriental medicine and they are improved. All of them had amnesia for $6{\sim}10\;hours$. During that time, they show behavioral changes and they are not on the state of unconsciousness. After recovery, they also forget what happen at the time. they have some emotional reason too. In conclusion, 4 cases of them belong to dissociative disorder and 2 other cases, TIA.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.179-186
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• 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.

• Speech Sciences
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• v.13 no.3
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• pp.7-18
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• 2006

Diagnosis of pathological voice is one of the important issues in biomedical applications of speech technology. This study focuses on the discrimination of voice disorder using HMM (Hidden Markov Model) for automatic detection between normal voice and vocal fold disorder voice. This is a non-intrusive, non-expensive and fully automated method using only a speech sample of the subject. Speech data from normal people and patients were collected. Mel-frequency filter cepstral coefficients (MFCCs) were modeled by HMM classifier. Different states (3 states, 5 states and 7 states), 3 mixtures and left to right HMMs were formed. This method gives an accuracy of 93.8% for train data and 91.7% for test data in the discrimination of normal and vocal fold disorder voice for sustained /a/.