@article{Jaafar2019135,
title = {Parent-report instruments for assessing feeding difficulties in children with neurological impairments: a systematic review},
author = {N H Jaafar and A Othman and N A Majid and S Harith and Z Zabidi-Hussin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052789833&doi=10.1111%2fdmcn.13986&partnerID=40&md5=d02a2bfcd15a25988b9c23855cd87444},
doi = {10.1111/dmcn.13986},
issn = {00121622},
year = {2019},
date = {2019-01-01},
journal = {Developmental Medicine and Child Neurology},
volume = {61},
number = {2},
pages = {135-144},
publisher = {Blackwell Publishing Ltd},
abstract = {Aim: This study aimed to review the psychometric properties and clinical application of parent-report instruments that assess feeding difficulties in children with neurological impairments. Method: Papers were identified through five electronic databases based on 15 keywords and were included if they met the following criteria: published in English, described the implementation of parent-report instruments, and included children with neurological impairments (either in the report or a related study population). Results: In total, 1220 relevant abstracts were screened and 22 full-text articles were evaluated. The following six parent-report instruments met the inclusion criteria: (1) Screening Tool of Feeding Problems applied to children, (2) Paediatric Eating Assessment Tool, (3) Paediatric Assessment Scale for Severe Feeding Problems, (4) Montreal Children's Hospital Feeding Scale, (5) Children's Eating Behaviour Inventory, and (6) Behavioural Paediatric Feeding Assessment Scale (BPFAS). Based on comprehensive psychometric testing and consistently good results, the BPFAS was considered the most valid and reliable instrument. The BPFAS also showed good clinical applicability because it was readily available, required a short administration time, and used a simple scoring system. Interpretation: We reviewed the available parent-report instruments for assessing feeding difficulties in children with neurological impairments. The BPFAS had the best psychometric properties and clinical applicability. What this paper adds: Six parent-report instruments were suitable for assessing feeding in children with neurological impairments. The Behavioural Paediatric Feeding Assessment Scale (BPFAS) has the strongest psychometric properties. The BPFAS also has good clinical applicability. © 2018 Mac Keith Press},
note = {cited By 1},
keywords = {Assessment of Humans, Autism, Behavioural Paediatric Feeding Assessment Scale, Caloric Intake, Child Behaviour, Child Parent Relation, Childhood Disease, Children, Children's Eating Behaviour Inventory, Complication, Construct Validity, Content Validity, Criterion Related Validity, Cystic Fibrosis, Eating Disorder, Enalapril Maleate, Eosinophilic Gastrointestinal Disorder, Esophagus Atresia, Feeding, Feeding and Eating Disorders, Feeding Behavior, Feeding Difficulty, Food Intake, Human, Nervous System Diseases, Neurologic Disease, Nutritional Assessment, Parents, Pediatric Assessment Scale for Severe Feeding Problem, Pediatric Eating Assessment Tool, Predictive Value, Preschool, Preschool Child, Priority Journal, Procedures, Psychology, Psychometrics, Psychometry, Quality of Life, Receiver Operating Characteristic, Review, Scoring System, Self Disclosure, Sensitivity and Specificity, Syndrome CHARGE, Systematic Review, Test Retest Reliability},
pubstate = {published},
tppubtype = {article}
}

@article{Toh201828,
title = {Accuracy of Modified Checklist for Autism in Toddlers (M-CHAT) in Detecting Autism and Other Developmental Disorders in Community Clinics},
author = {T -H Toh and V W -Y Tan and P S -T Lau and A Kiyu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028764085&doi=10.1007%2fs10803-017-3287-x&partnerID=40&md5=21bce2407197b8b1e43b4420d274861b},
doi = {10.1007/s10803-017-3287-x},
issn = {01623257},
year = {2018},
date = {2018-01-01},
journal = {Journal of Autism and Developmental Disorders},
volume = {48},
number = {1},
pages = {28-35},
publisher = {Springer New York LLC},
abstract = {This study determined the accuracy of Modified Checklist for Autism in Toddlers (M-CHAT) in detecting toddlers with autism spectrum disorder (ASD) and other developmental disorders (DD) in community mother and child health clinics. We analysed 19,297 eligible toddlers (15–36 months) who had M-CHAT performed in 2006–2011. Overall sensitivities for detecting ASD and all DD were poor but better in the 21 to <27 months and 27–36-month age cohorts (54.5–64.3%). Although positive predictive value (PPV) was poor for ASD, especially the younger cohort, positive M-CHAT helped in detecting all DD (PPV = 81.6%). This suggested M-CHAT for screening ASD was accurate for older cohorts (>21 months) and a useful screening tool for all DD. © 2017, Springer Science+Business Media, LLC.},
note = {cited By 9},
keywords = {Article, Autism, Autism Assessment, Autism Spectrum Disorders, Checklist, Children, Cohort Analysis, Cohort Studies, Community Health Centers, Developmental Disorders, Diagnostic Accuracy, Female, Health Center, Human, Infant, Major Clinical Study, Malaysia, Male, Mass Screening, Modified Checklist for Autism in Toddlers, Pediatric Hospital, Predictive Value, Preschool, Preschool Child, Priority Journal, Procedures, Psychology, Retrospective Studies, Retrospective Study, Sensitivity and Specificity, Standards, Toddler},
pubstate = {published},
tppubtype = {article}
}

@article{Hnoonual2017,
title = {Chromosomal microarray analysis in a cohort of underrepresented population identifies SERINC2 as a novel candidate gene for autism spectrum disorder},
author = {A Hnoonual and W Thammachote and T Tim-Aroon and K Rojnueangnit and T Hansakunachai and T Sombuntham and R Roongpraiwan and J Worachotekamjorn and J Chuthapisith and S Fucharoen and D Wattanasirichaigoon and N Ruangdaraganon and P Limprasert and N Jinawath},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029864969&doi=10.1038%2fs41598-017-12317-3&partnerID=40&md5=3c1b6a0c064665aab8ace8e8f58c2b01},
doi = {10.1038/s41598-017-12317-3},
issn = {20452322},
year = {2017},
date = {2017-01-01},
journal = {Scientific Reports},
volume = {7},
number = {1},
publisher = {Nature Publishing Group},
abstract = {Chromosomal microarray (CMA) is now recognized as the first-tier genetic test for detection of copy number variations (CNVs) in patients with autism spectrum disorder (ASD). The aims of this study were to identify known and novel ASD associated-CNVs and to evaluate the diagnostic yield of CMA in Thai patients with ASD. The Infinium CytoSNP-850K BeadChip was used to detect CNVs in 114 Thai patients comprised of 68 retrospective ASD patients (group 1) with the use of CMA as a second line test and 46 prospective ASD and developmental delay patients (group 2) with the use of CMA as the first-tier test. We identified 7 (6.1%) pathogenic CNVs and 22 (19.3%) variants of uncertain clinical significance (VOUS). A total of 29 patients with pathogenic CNVs and VOUS were found in 22% (15/68) and 30.4% (14/46) of the patients in groups 1 and 2, respectively. The difference in detected CNV frequencies between the 2 groups was not statistically significant (Chi square = 1.02},
note = {cited By 6},
keywords = {Adolescent, Autism, Autism Spectrum Disorders, Children, Chromosomal Mapping, Chromosome Mapping, Cohort Analysis, Cohort Studies, Copy Number Variation, DNA Copy Number Variations, Female, Genetic Predisposition, Genetic Predisposition to Disease, Genetics, Human, Infant, Male, Membrane Protein, Membrane Proteins, Microarray Analysis, Polymorphism, Preschool, Preschool Child, Procedures, SERINC2 Protein, Single Nucleotide, Single Nucleotide Polymorphism},
pubstate = {published},
tppubtype = {article}
}

@article{Shuib201777,
title = {Duplication 17p11.2 (Potocki-Lupski syndrome) in a child with developmental delay},
author = {S Shuib and N N Saaid and Z Zakaria and J Ismail and Z Abdul Latiff},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037028880&partnerID=40&md5=624b87d1e9ebac2d1bf66b4d30c0f6e9},
issn = {01268635},
year = {2017},
date = {2017-01-01},
journal = {Malaysian Journal of Pathology},
volume = {39},
number = {1},
pages = {77-81},
publisher = {Malaysian Society of Pathologists},
abstract = {Potocki-Lupski syndrome (PTLS), also known as duplication 17p11.2 syndrome, trisomy 17p11.2 or dup(17)(p11.2p11.2) syndrome, is a developmental disorder and a rare contiguous gene syndrome affecting 1 in 20,000 live births. Among the key features of such patients are autism spectrum disorder, learning disabilities, developmental delay, attention-deficit disorder, infantile hypotonia and cardiovascular abnormalities. Previous studies using microarray identified variations in the size and extent of the duplicated region of chromosome 17p11.2. However, there are a few genes which are considered as candidates for PTLS which include RAI1, SREBF1, DRG2, LLGL1, SHMT1 and ZFP179. In this report, we investigated a case of a 3-year-old girl who has developmental delay. Her chromosome analysis showed a normal karyotype (46,XX). Analysis using array CGH (4X44 K, Agilent USA) identified an ~4.2 Mb de novo duplication in chromosome 17p11.2. The result was confirmed by fluorescence in situ hybridization (FISH) using probes in the critical PTLS region. This report demonstrates the importance of microarray and FISH in the diagnosis of PTLS. © 2017, Malaysian Society of Pathologists. All rights reserved.},
note = {cited By 0},
keywords = {Abnormalities, Agarose, Article, Autism, Autism Spectrum Disorders, Blood Culture, Case Report, Children, Chromosome 17, Chromosome Analysis, Chromosome Disorder, Chromosome Duplication, Chromosomes, Clinical Article, Comparative Genomic Hybridization, Developmental Delay, Electrophoresis, Female, Fluorescence, Fluorescence in Situ Hybridization, Gene, Gene Identification, Genetics, Genomic DNA, Human, In Situ Hybridization, Lymphocyte Culture, Microarray Analysis, Multiple, Multiple Malformation Syndrome, Pair 17, Phenotype, Potocki Lupski Syndrome, Preschool, Preschool Child, Procedures, RAI1 Gene, Ultraviolet Spectrophotometry},
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{Banire20153069,
title = {Visual Hybrid Development Learning System (VHDLS) Framework for Children with Autism},
author = {B Banire and N Jomhari and R Ahmad},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941942795&doi=10.1007%2fs10803-015-2469-7&partnerID=40&md5=3c5ecc776725aea4e585e17a1ae805c7},
doi = {10.1007/s10803-015-2469-7},
issn = {01623257},
year = {2015},
date = {2015-01-01},
journal = {Journal of Autism and Developmental Disorders},
volume = {45},
number = {10},
pages = {3069-3084},
publisher = {Springer New York LLC},
abstract = {The effect of education on children with autism serves as a relative cure for their deficits. As a result of this, they require special techniques to gain their attention and interest in learning as compared to typical children. Several studies have shown that these children are visual learners. In this study, we proposed a Visual Hybrid Development Learning System (VHDLS) framework that is based on an instructional design model, multimedia cognitive learning theory, and learning style in order to guide software developers in developing learning systems for children with autism. The results from this study showed that the attention of children with autism increased more with the proposed VHDLS framework. © 2015, Springer Science+Business Media New York.},
note = {cited By 7},
keywords = {Article, Attention, Autism, Autism Spectrum Disorders, Children, Computer Interface, Education, Education of Intellectually Disabled, Educational Model, Feedback System, Female, Human, Learning, Male, Models, Occupational Therapist, Preschool, Preschool Child, Priority Journal, Procedures, Psychology, Quality of Life, Treatment Duration, User Interfaces, Visual Hybrid Development Learning System, Visual Stimulation},
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}
}

@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}
}