@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{Hameed2017,
title = {Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm},
author = {S S Hameed and R Hassan and F F Muhammad},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033361187&doi=10.1371%2fjournal.pone.0187371&partnerID=40&md5=f9260d41165145f229a3cf157699635e},
doi = {10.1371/journal.pone.0187371},
issn = {19326203},
year = {2017},
date = {2017-01-01},
journal = {PLoS ONE},
volume = {12},
number = {11},
publisher = {Public Library of Science},
abstract = {In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy. © 2017 Hameed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.},
note = {cited By 11},
keywords = {Accuracy, Algorithms, Article, Autism, Autism Spectrum Disorders, CAPS2 Gene, Classification (of information), Classifier, Experimental Study, Gene, Gene Expression, Gene Identification, Genetic Association, Genetic Procedures, Genetic Risk, Genetics, Geometric Binary Particle Swarm Optimization Support Vector Machine Algorithm, Human, RIsk Assessment, Standardization, Statistical Filter, Statistical Parameters, Statistics, Support Vector Machines},
pubstate = {published},
tppubtype = {article}
}

@article{Hasan201755,
title = {The analysis of three-dimensional ground reaction forces during gait in children with autism spectrum disorders},
author = {C Z C Hasan and R Jailani and N Md Tahir and S Ilias},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015640386&doi=10.1016%2fj.ridd.2017.02.015&partnerID=40&md5=d6a9839cda7f62bcce9bdcca33d3d33b},
doi = {10.1016/j.ridd.2017.02.015},
issn = {08914222},
year = {2017},
date = {2017-01-01},
journal = {Research in Developmental Disabilities},
volume = {66},
pages = {55-63},
publisher = {Elsevier Inc.},
abstract = {Minimal information is known about the three-dimensional (3D) ground reaction forces (GRF) on the gait patterns of individuals with autism spectrum disorders (ASD). The purpose of this study was to investigate whether the 3D GRF components differ significantly between children with ASD and the peer controls. 15 children with ASD and 25 typically developing (TD) children had participated in the study. Two force plates were used to measure the 3D GRF data during walking. Time-series parameterization techniques were employed to extract 17 discrete features from the 3D GRF waveforms. By using independent t-test and Mann-Whitney U test, significant differences (p < 0.05) between the ASD and TD groups were found for four GRF features. Children with ASD demonstrated higher maximum braking force, lower relative time to maximum braking force, and lower relative time to zero force during mid-stance. Children with ASD were also found to have reduced the second peak of vertical GRF in the terminal stance. These major findings suggest that children with ASD experience significant difficulties in supporting their body weight and endure gait instability during the stance phase. The findings of this research are useful to both clinicians and parents who wish to provide these children with appropriate treatments and rehabilitation programs. © 2017 Elsevier Ltd},
note = {cited By 8},
keywords = {Age Distribution, Article, Autism, Autism Spectrum Disorders, Biomechanical Phenomena, Biomechanics, Body Equilibrium, Body Height, Body Mass, Body Weight, Children, Clinical Article, Controlled Study, Disease Assessment, Female, Gait, Gait Analysis, Gait Disorder, Ground Reaction Forces, Human, Imaging, Leg Length, Malaysia, Male, Neurologic Examination, Pathophysiology, Physiology, Postural Balance, Procedures, Psychology, Statistics, Three-Dimensional, Three-Dimensional Imaging, Three-Dimentional Ground Reaction Force, Walking},
pubstate = {published},
tppubtype = {article}
}

@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{MohdRoffeei2015375,
title = {Seeking social support on Facebook for children with Autism Spectrum Disorders (ASDs)},
author = {S H Mohd Roffeei and N Abdullah and S K R Basar},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924517643&doi=10.1016%2fj.ijmedinf.2015.01.015&partnerID=40&md5=7296825093cbf87388b5b65023e59371},
doi = {10.1016/j.ijmedinf.2015.01.015},
issn = {13865056},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Medical Informatics},
volume = {84},
number = {5},
pages = {375-385},
publisher = {Elsevier Ireland Ltd},
abstract = {Purpose: This study examined the types of social support messages exchanged between parents and/or caregivers of children with Autism Spectrum Disorders (ASDs) who communicate via Facebook (FB); it studies two autism support groups: Autism Malaysia (AM) and Autism Children Club (ACA). Method: A total of 3637 messages including both postings (381) and comments (3256) were gathered from August to November 2013. The study employed a deductive content-analysis approach. The qualitative data were analyzed for social support themes adapted from the Social Support Behavior Code (SSBC). Before collecting the data, email was sent to the FB groups' moderators to gain formal consent from the members. Result: The finding indicated that the highest percentage of messages offered dealt with Informational support (30.7%) followed by Emotional support (27.8%). Network and Esteem support messages were responsible for 20.97% and 20.2%, respectively. Tangible Assistance was the least frequent category (0.4%). A majority of these messages discussed and addressed challenges and difficulties associated with caring and raising ASD children, as well as issues such as children's social lives and self-care routines. Conclusion: Understandings of how FB is used to seek social support could impact supporting and maintaining effective communication among parents and/or caregivers of children with ASDs. This information could also improve approaches used by health professionals in developing, improving and evaluating social support systems for parents/caregivers. © 2015 Elsevier Ireland Ltd.},
note = {cited By 43},
keywords = {Article, ASD, Autism, Autism Spectrum Disorders, Caregiver, Children, Consumer Health Information, Content Analysis, Diseases, E-mail, Facebook, Family, Friendship, Human, Internet, Parents, Patient Referral, Priority Journal, Psychology, Qualitative Analysis, Self Esteem, Social Media, Social Networking, Social Support, Statistics, Support Group, Telemedicine, Utilization},
pubstate = {published},
tppubtype = {article}
}

@article{Selvaraj2013,
title = {Classification of emotional states from electrocardiogram signals: A non-linear approach based on hurst},
author = {J Selvaraj and M Murugappan and K Wan and S Yaacob},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879017985&doi=10.1186%2f1475-925X-12-44&partnerID=40&md5=18c5309ac9f3017f455480f1ff732a30},
doi = {10.1186/1475-925X-12-44},
issn = {1475925X},
year = {2013},
date = {2013-01-01},
journal = {BioMedical Engineering Online},
volume = {12},
number = {1},
publisher = {BioMed Central Ltd.},
abstract = {Background: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.Methods: Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature 'Hurst' was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers - Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.Results: Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.Conclusions: The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. © 2013 Selvaraj et al.; licensee BioMed Central Ltd.},
note = {cited By 42},
keywords = {Adolescent, Adult, Aged, Article, Audio-Visual Stimulus, Autonomous Nervous Systems, Children, Classification Accuracy, Computer Based Training, Computer-Assisted, Electrocardiogram Signal, Electrocardiography, Emotion, Female, Fuzzy K-nearest Neighbor, Higher-Order Statistic (HOS), Human, Intellectual Disability, Interactive Computer Systems, Methodology, Middle Aged, Nonlinear Dynamics, Nonlinear System, Procedures, Real Time Systems, Signal Processing, Statistics, Young Adult},
pubstate = {published},
tppubtype = {article}
}