@article{Sarwar20191824,
title = {Social support, optimism, parental self-efficacy and wellbeinin mothers of children with autism spectrum disorder},
author = {F Sarwar and S A Panatik and A Rajab and N Nordin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074977478&doi=10.5958%2f0976-5506.2019.02719.0&partnerID=40&md5=6760a63e9eca52a1bb463dc10bd5abe6},
doi = {10.5958/0976-5506.2019.02719.0},
issn = {09760245},
year = {2019},
date = {2019-01-01},
journal = {Indian Journal of Public Health Research and Development},
volume = {10},
number = {9},
pages = {1824-1829},
publisher = {Indian Journal of Public Health Research and Development},
abstract = {It was hypothesized that optimism, self-efficacy and social support are positive predictors of life satisfaction and positive affect and negative predictors of perceived stress and negative affect. Data was collected by survey method from 47 mothers of autistic children in Lahore and Faisalabad. Hypotheses were tested on four models of four dependant variables using hierarchical regression analysis. Results depicted that parental self-efficacy was a significant predictor of all four dependant variables, social support was a significant predictor of life satisfaction and perceived stress, while optimism only significantly predicted variance in life satisfaction. The study was first of its type to be done with a sample in Pakistani context and has important implications for clinical psychologist. They can plan interventions to enhance subjective wellbeing and reduce stress directly or indirectly by focusing on antecedents tested in the study. © 2019, Indian Journal of Public Health Research and Development. All rights reserved.},
note = {cited By 0},
keywords = {Adult, Article, Assessment of Humans, Autism, Children, Correlation Analysis, Cross-Sectional Study, Female, Human, Life Orientation Test, Life Satisfaction, Likert Scale, Male, Maternal Behavior, Multidimensional Scale of Percieved Social Support, Optimism, Parenting Sense of Competence, Perceived Stress Scale, Positive and Negative Affect Schedule, Questionnaires, Satisfaction with Life Scale, Self Concept, Social Support, Wellbeing},
pubstate = {published},
tppubtype = {article}
}

@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}
}