2015 |
Yussof, H; Salleh, M H; Miskam, M A; Shamsuddin, S; Omar, A R ASKNAO apps targeting at social skills development for children with autism Conference 2015-October , IEEE Computer Society, 2015, ISSN: 21618070, (cited By 3). Abstract | Links | BibTeX | Tags: Automation, Children with Autism, Communication Skills, Diseases, Education, Social Skills @conference{Yussof2015973, title = {ASKNAO apps targeting at social skills development for children with autism}, author = {H Yussof and M H Salleh and M A Miskam and S Shamsuddin and A R Omar}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952770737&doi=10.1109%2fCoASE.2015.7294225&partnerID=40&md5=bb2d8f8a5d54a457dec4137e1a55514a}, doi = {10.1109/CoASE.2015.7294225}, issn = {21618070}, year = {2015}, date = {2015-01-01}, journal = {IEEE International Conference on Automation Science and Engineering}, volume = {2015-October}, pages = {973-978}, publisher = {IEEE Computer Society}, abstract = {This paper aims to review the ASKNAO apps targeting at social skills of children with autism. The ASKNAO Apps is a system that designed to teach children with autism the basic skills that are naturally learned by the typical children. Since ASKNAO apps is a commercial based system, the contents are yet to be categorized technically and specifically in accordance to the three autism criteria which are social skills, communication skills and repetitive behavior. By taking the first step in identifying the Apps suitability focusing on social skills, further study on application and assessment of ASKNAO can be conducted to teach the child in the direction of the user's requirements. © 2015 IEEE.}, note = {cited By 3}, keywords = {Automation, Children with Autism, Communication Skills, Diseases, Education, Social Skills}, pubstate = {published}, tppubtype = {conference} } This paper aims to review the ASKNAO apps targeting at social skills of children with autism. The ASKNAO Apps is a system that designed to teach children with autism the basic skills that are naturally learned by the typical children. Since ASKNAO apps is a commercial based system, the contents are yet to be categorized technically and specifically in accordance to the three autism criteria which are social skills, communication skills and repetitive behavior. By taking the first step in identifying the Apps suitability focusing on social skills, further study on application and assessment of ASKNAO can be conducted to teach the child in the direction of the user's requirements. © 2015 IEEE. |
2014 |
Bhat, S; Acharya, U R; Adeli, H; Bairy, G M; Adeli, A Automated diagnosis of autism: In search of a mathematical marker Journal Article Reviews in the Neurosciences, 25 (6), pp. 851-861, 2014, ISSN: 03341763, (cited By 34). Abstract | Links | BibTeX | Tags: Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis @article{Bhat2014851, title = {Automated diagnosis of autism: In search of a mathematical marker}, author = {S Bhat and U R Acharya and H Adeli and G M Bairy and A Adeli}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925286949&doi=10.1515%2frevneuro-2014-0036&partnerID=40&md5=04858a5c9860e9027e3113835ca2e11f}, doi = {10.1515/revneuro-2014-0036}, issn = {03341763}, year = {2014}, date = {2014-01-01}, journal = {Reviews in the Neurosciences}, volume = {25}, number = {6}, pages = {851-861}, publisher = {Walter de Gruyter GmbH}, abstract = {Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH.}, note = {cited By 34}, keywords = {Algorithms, Article, Autism, Autism Spectrum Disorders, Automation, Biological Model, Brain, Chaos Theory, Correlation Analysis, Detrended Fluctuation Analysis, Disease Marker, Electrode, Electroencephalogram, Electroencephalography, Entropy, Fourier Transformation, Fractal Analysis, Frequency Domain Analysis, Human, Mathematical Analysis, Mathematical Marker, Mathematical Parameters, Models, Neurologic Disease, Neurological, Nonlinear Dynamics, Nonlinear System, Pathophysiology, Priority Journal, Procedures, Signal Processing, Statistical Model, Time, Time Frequency Analysis, Wavelet Analysis}, pubstate = {published}, tppubtype = {article} } Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-theart review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEGbased diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder. © 2014 Walter de Gruyter GmbH. |
Testingadminnaacuitm2020-05-28T06:49:14+00:00
2015 |
ASKNAO apps targeting at social skills development for children with autism Conference 2015-October , IEEE Computer Society, 2015, ISSN: 21618070, (cited By 3). |
2014 |
Automated diagnosis of autism: In search of a mathematical marker Journal Article Reviews in the Neurosciences, 25 (6), pp. 851-861, 2014, ISSN: 03341763, (cited By 34). |