T. Jayasree^1,*, S.Emerald Shia²

Sound & Vibration, Vol.55, No.2, pp. 141-161, 2021, DOI:10.32604/sv.2021.011734

Abstract This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks (FFNN). The important pathological voices such as Autism Spectrum Disorder (ASD) and Down Syndrome (DS) are considered for analysis. These pathological voices are known to manifest in different ways in the speech of children and adults. Therefore, it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects. The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques. In this work, three… More >

Mahiye Uluyagmur Ozturk^a, Ayse Rodopman Arman^b, Gresa Carkaxhiu Bulut^c, Onur Tugce Poyraz Findik^b, Sultan Seval Yilmaz^d, Herdem Aslan Genc^b, M. Yanki Yazgan^e,f, Umut Teker^a, Zehra Cataltepe^a

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 891-905, 2018, DOI:10.31209/2018.100000058

Abstract Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data (APL). In order to detect… More >

Jessica L. Bean Jaworski, Thomas Flynn, Nancy Burnham, Jesse L. Chittams, Therese Sammarco, Marsha Gerdes, Judy C. Bernbaum, Robert R. Clancy, Cynthia B. Solot, Elaine H. Zackai, Donna M. McDonald-McGinn, J. William Gaynor

Congenital Heart Disease, Vol.12, No.4, pp. 421-429, 2017, DOI:10.1111/chd.12461

Abstract Objective: Atypical development, behavioral difficulties, and academic underachievement are common morbidities in children with a history of congenital heart defects and impact quality of life. Language and social-cognitive deficits have been described, which are associated with autism spectrum disorders. The current study aimed to assess the rates of autism spectrum disorders in a large sample of children with a history of congenital heart defects and to assess medical, behavioral, and individual factors that may be associated with the risk of autism spectrum disorders.
Design: Participants included 195 children with a history of congenital heart defects, who are followed in a… More >