@article{Ramachandram2019372,
title = {Clinical characteristics and demographic profile of children with autism spectrum disorder (Asd) at child development clinic (cdc), penang hospital, malaysia},
author = {S Ramachandram},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073688991&partnerID=40&md5=3ed147d56181ccd44321c47629a4aa54},
issn = {03005283},
year = {2019},
date = {2019-01-01},
journal = {Medical Journal of Malaysia},
volume = {74},
number = {5},
pages = {372-376},
publisher = {Malaysian Medical Association},
abstract = {Objective: To explore socio-demographics and clinical characteristics of children with Autism Spectrum Disorder (ASD) at Child Development Clinic (CDC), Penang Hospital. Study design: A record review study of 331 children with ASD attending CDC, Penang Hospital from September 2013 to April 2017. Results: Out of 331 children with ASD, 82.5% were males, 17.5% females, with male to female ratio of 4.7:1. Mean age at consultation was 5 years and 6 months (SD 31.68 months) with age range from 19 months to 18 years and 4 months. 85.8% were term infants with normal birth weight. History of speech regression was noted in 14.8%, epilepsy and genetic disorders in 9.4% and 5.7% respectively. Sleep problems was reported in 29.3%, dietary issues 22.1%, challenging behaviour 24.2% and ADHD 14.2%. Mean age of the father and mother at birth was 33.6 and 31.6 years respectively. Conclusion: In this study, we report a higher male to female ratio and mean age at referral with some similar rates of neurodevelopmental and medical comorbidities and relatively younger parental age with higher parental education levels. © 2019, Malaysian Medical Association. All rights reserved.},
note = {cited By 0},
keywords = {Adolescent, Article, Asthma, Autism, Birth Weight, Child Development, Children, Chinese, Conception, Demography, Diet Restriction, DSM-5, Eczema, Education, Educational Status, Epilepsy, Female, Genetic Disorder, Heart Atrium Septum Defect, Heart Ventricle Septum Defect, Human, Indian, Major Clinical Study, Malay, Male, Medical Record Review, Penang, Prematurity, Speech Disorder, Upper Respiratory Tract Congestion, Wakefulness},
pubstate = {published},
tppubtype = {article}
}

@article{Hariharan201839,
title = {Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification},
author = {M Hariharan and R Sindhu and V Vijean and H Yazid and T Nadarajaw and S Yaacob and K Polat},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036611215&doi=10.1016%2fj.cmpb.2017.11.021&partnerID=40&md5=1f3b17817b00f07cadad6eb61c0f4bf9},
doi = {10.1016/j.cmpb.2017.11.021},
issn = {01692607},
year = {2018},
date = {2018-01-01},
journal = {Computer Methods and Programs in Biomedicine},
volume = {155},
pages = {39-51},
publisher = {Elsevier Ireland Ltd},
abstract = {Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals. © 2017 Elsevier B.V.},
note = {cited By 21},
keywords = {Accidents, Algorithms, Article, Artificial Neural Network, Asphyxia, Binary Dragonfly Optimization Aalgorithm, Classification (of information), Classification Algorithm, Classifier, Coding, Computer-Assisted, Constants and Coefficients, Crying, Database Systems, Databases, Deafness, Diagnosis, Energy, Entropy, Extraction, Extreme Learning Machine, Factual, Factual Database, Feature Extraction, Feature Selection Methods, Fuzzy System, Hearing Impairment, Human, Hunger, Infant, Infant Cry, Infant Cry Classifications, Jaundice, Kernel Method, Learning, Linear Predictive Coding, Machine Learning, Mathematical Transformations, Mel Frequency Cepstral Coefficient, Mel Frequency Cepstral Coefficients, Multi-Class Classification, Neural Networks, Nonlinear Dynamics, Nonlinear System, Optimization, Pain, Pathophysiology, Prematurity, Reproducibility, Reproducibility of Results, Signal Processing, Speech Recognition, Wavelet Analysis, Wavelet Packet, Wavelet Packet Transforms},
pubstate = {published},
tppubtype = {article}
}