@article{Adib2018,
title = {Translation and validation of the malay version of the parents’ satisfaction scale (Pss-m) for assessment of caregivers’ satisfaction with health care services for children with autism spectrum disorder},
author = {N A N Adib and M I Ibrahim and A A Rahman and R S Bakar and N A Yahaya and S Hussin and W N Arifin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056090545&doi=10.3390%2fijerph15112455&partnerID=40&md5=53650806d46343cc3e95c9b30442f79c},
doi = {10.3390/ijerph15112455},
issn = {16617827},
year = {2018},
date = {2018-01-01},
journal = {International Journal of Environmental Research and Public Health},
volume = {15},
number = {11},
publisher = {MDPI AG},
abstract = {Background: A Malay version of Parent Satisfaction Scale (PSS-M) is needed to investigate the factors contributing to the Malay caregivers’ satisfaction with health care management for children with autism spectrum disorder (ASD). The aim of the study is to translate and validate the questionnaire to assess the caregivers’ satisfaction on health care services. Methods: A cross-sectional study was conducted among 110 caregivers of children with ASD aged between 2 and 17 years old that received treatment at two tertiary care centres in Kelantan. Permission to use the original version of the PSS questionnaire was obtained. The original English version of the PSS was translated into a Malay version following the 10 steps proposed by an established guideline. Pre-testing of the PSS was carried out with 30 caregivers before confirmatory factor analysis (CFA) was established using 110 caregivers. They were asked to assess their understanding of the questionnaire. The one-dimensional questionnaire consists of 11 items, including staff attitudes, availability of staff, supportiveness, and helpfulness. The 5-point Likert scale provided ratings from 1 (strongly disagree) to 5 (strongly agree). Confirmatory factor analysis was performed using a robust maximum likelihood estimator. Results: The analysis showed model fit data with good reliability. Conclusion: The PSS-M shows overall model fitness based on specific indices, with good construct validity and excellent absolute reliability to determine the satisfaction level of caregivers of children with ASD with respect to health care services. © 2018, MDPI AG. All rights reserved.},
note = {cited By 2},
keywords = {Adult, Article, Autism, Autism Spectrum Disorders, Caregiver, Child Care, Child Parent Relation, Children, Cross-Sectional Study, Factor Analysis, Female, Guideline, Health Personnel Attitude, Health Service, Health Worker, Human, Kelantan, Likelihood Functions, Likert Scale, Malaysia, Male, Maximum Likelihood Analysis, Mental Health, Mental Health Service, Parents, Parents Satisfaction Scale Malay Version, Personal Satisfaction, Practice Guideline, Psychological Rating Scale, Psychology, Publication, Questionnaires, Reproducibility, Reproducibility of Results, Satisfaction, Statistical Model, Statistics, Surveys, Tertiary Care Center, Translations, Validation Study, West Malaysia},
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}
}