@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{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}
}