@article{Hasan201774,
title = {Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder},
author = {C Z C Hasan and R Jailani and N Md Tahir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043500605&partnerID=40&md5=6f2ffe7c2f5daf9fd02d4456acb94438},
issn = {19984510},
year = {2017},
date = {2017-01-01},
journal = {International Journal of Biology and Biomedical Engineering},
volume = {11},
pages = {74-79},
publisher = {North Atlantic University Union NAUN},
abstract = {Automated differentiation of ASD gait from normal gait patterns is important for early diagnosis as well as ensuring rapid quantitative clinical decision and appropriate treatment planning. This study explores the use of statistical feature selection approaches and artificial neural networks (ANN) for automated identification of gait deviations in children with ASD, on the basis of dominant gait features derived from the three-dimensional (3D) joint kinematic data. The gait data from 30 ASD children and 30 normal healthy children were measured using a state-of-the-art 3D motion analysis system during self-selected speed barefoot walking. Kinematic gait features from the sagittal, frontal and transverse joint angles waveforms at the pelvis, hip, knee, and ankle were extracted using time-series parameterization. Two statistical feature selection techniques, namely the between-group tests (independent samples t-test and Mann-Whitney U test) and the stepwise discriminant analysis (SWDA) were adopted as feature selector to select the meaningful gait features that were then used to train the ANN. The 10-fold cross-validation test results indicate that the selected gait features using SWDA technique are more reliable for ASD gait classification with 91.7% accuracy, 93.3% sensitivity, and 90.0% specificity. The findings of the current study demonstrate that kinematic gait features with the combination of SWDA feature selector and ANN classifier would serve as a potential tool for early diagnosis of gait deviations in children with ASD as well as provide support to clinicians and therapists for making objective, accurate, and rapid clinical decisions that lead to the appropriate targeted treatments. © 2017 North Atlantic University Union NAUN. All Rights Reserved.},
note = {cited By 1},
keywords = {Article, Artificial Neural Network, Autism, Body Height, Body Mass, Children, Clinical Article, Controlled Study, Discriminant Analysis, Early Diagnosis, Female, Gait, Gait Analysis, Gait Disorder, Human, Learning, Male, Pediatrics, School Child, Statistical Analysis, Statistics, Time Series Analysis},
pubstate = {published},
tppubtype = {article}
}

@article{Bhat2014841,
title = {Autism: Cause factors, early diagnosis and therapies},
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-s2.0-84925284617&doi=10.1515%2frevneuro-2014-0056&partnerID=40&md5=caaa32e66af70e70ec325241d01564c9},
doi = {10.1515/revneuro-2014-0056},
issn = {03341763},
year = {2014},
date = {2014-01-01},
journal = {Reviews in the Neurosciences},
volume = {25},
number = {6},
pages = {841-850},
publisher = {Walter de Gruyter GmbH},
abstract = {Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and stereotyped behavior are the major autistic symptoms, visible after a certain age. It is one of the fastest growing disabilities. Its current prevalence rate in the U.S. estimated by the Centers for Disease Control and Prevention is 1 in 68 births. The genetic and physiological structure of the brain is studied to determine the pathology of autism, but diagnosis of autism at an early age is challenging due to the existing phenotypic and etiological heterogeneity among ASD individuals. Volumetric and neuroimaging techniques are explored to elucidate the neuroanatomy of the ASD brain. Nuroanatomical, neurochemical, and neuroimaging biomarkers can help in the early diagnosis and treatment of ASD. This paper presents a review of the types of autism, etiologies, early detection, and treatment of ASD. © 2014 Walter de Gruyter GmbH.},
note = {cited By 52},
keywords = {4 Aminobutyric Acid, Adolescent, Agenesis of Corpus Callosum, Animal Assisted Therapy, Anticonvulsive Agent, Article, Assistive Technology, Attention, Autism, Autism Spectrum Disorders, Behaviour Therapy, Biological Marker, Brain, Child Development Disorders, Children, Cognition, Cystine, Developmental Disorders, Diseases, Dolphin, Dolphin Assisted Therapy, DSM-5, Early Diagnosis, Emotion, Facial Expression, Functional Magnetic Resonance Imaging, Functional Neuroimaging, Gaze, Glutathione, Glutathione Disulfide, Human, Infant, Interpersonal Communication, Methionine, Nervous System Inflammation, Neurobiology, Neurofeedback, Oxidative Stress, Pervasive, Physiology, Preschool Child, Priority Journal, Psychoeducation, School Child, Social Interactions, Speech Therapy, Virtual Reality, Zonisamide},
pubstate = {published},
tppubtype = {article}
}