@conference{Ilias2017275,
title = {Feature extraction of autism gait data using principal component analysis and linear discriminant analysis},
author = {S Ilias and N M Tahir and R Jailani},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034081031&doi=10.1109%2fIEACON.2016.8067391&partnerID=40&md5=7deaef6538413df7bfaf7cf723001d72},
doi = {10.1109/IEACON.2016.8067391},
isbn = {9781509009251},
year = {2017},
date = {2017-01-01},
journal = {IEACon 2016 - 2016 IEEE Industrial Electronics and Applications Conference},
pages = {275-279},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In this research, the application of machine learning approach specifically support vector machine along with principal component analysis and linear discriminant analysis as feature extractions are evaluated and validated in discriminating gait features between normal subjects and autism children. Gait features of 32 normal and 12 autism children were recorded and analyzed using VICON motion analysis system and a force platform during normal walking. Here, twenty one gait features describing the three types of gait characteristics namely basic, kinetic and kinematic in these children are extracted. Further, with these gait features as input during classification, the ability of SVM as classifier are investigated using three different kernel functions specifically linear, polynomial, and radial basis. Results showed that LDA as feature extraction is the highest accuracy with kinematic parameters as gait features along with polynomial function as kernel for the SVM classifier. This finding proven that LDA is suitable as feature extraction and SVM is indeed apt as gait classifier in classifying the gait pattern autism and normal children. © 2016 IEEE.},
note = {cited By 0},
keywords = {Classification (of information), Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Image Retrieval, Industrial Electronics, Kernel Function, Kinematic Parameters, Kinematics, Learning, Linear Discriminant Analysis, Machine Learning Approaches, Motion Analysis System, Polynomial Functions, Principal Component Analysis, Support Vector Machines, SVM Classifiers},
pubstate = {published},
tppubtype = {conference}
}

@conference{Ilias201767,
title = {Linear Discriminant Analysis in Classifying Walking Gait of Autistic Children},
author = {S Ilias and N M Tahir and R Jailani and C Z C Hasan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048377850&doi=10.1109%2fEMS.2017.22&partnerID=40&md5=06de53be2b4f3976ddcc420067ab6e44},
doi = {10.1109/EMS.2017.22},
isbn = {9781538614099},
year = {2017},
date = {2017-01-01},
journal = {Proceedings - UKSim-AMSS 11th European Modelling Symposium on Computer Modelling and Simulation, EMS 2017},
pages = {67-72},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The aim of this research is to investigate the effectiveness between Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) along with neural network (NN) in classifying the gait of autistic children as compared to control group. Twelve autistic children and thirty two normal children participated in this study. Firstly the walking gait of these two groups are acquired using VICON Motion Analysis System to extract the three dimensional (3D) gait features that comprised of 21 gait features namely five features from basic temporal spatial, five features represented the kinetic parameters and twelve features from kinematic. Further, PCA and LDA are utilized as feature extraction in determining the significant features among these gait features. With NN as classifier, results showed that LDA as feature extraction outperform PCA for classification of autism versus normal children namely kinematic gait patterns attained 98.44% accuracy followed by basic temporal spatial gait features with accuracy of 87.5%. © 2017 IEEE.},
note = {cited By 0},
keywords = {Autism, Autistic Children, Children with Autism, Discriminant Analysis, Diseases, Extraction, Feature Extraction, Gait Analysis, Gait Classification, Kinematics, Linear Discriminant Analysis, Motion Analysis System, Neural Networks, Principal Component Analysis, Three-Dimensional},
pubstate = {published},
tppubtype = {conference}
}

@conference{Hoole2012541,
title = {Autism, EEG and brain electromagnetics research},
author = {P R P Hoole and K Pirapaharan and S A Basar and R Ismail and D L D A Liyanage and S S H M U L Senanayake and S R H Hoole},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876771339&doi=10.1109%2fIECBES.2012.6498036&partnerID=40&md5=9f9390b30b859a90936c66699c1a5115},
doi = {10.1109/IECBES.2012.6498036},
isbn = {9781467316668},
year = {2012},
date = {2012-01-01},
journal = {2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012},
pages = {541-543},
abstract = {There has been a significant increase in the incidence of autism. We report the work on autism by our international group, on the growing attention paid to EEG based diagnosis and the interest in tracing EEG changes to brain electromagnetic signals (BEMS), seeking the cause of autism and the brain regions of its origin. The time- and frequency domain and principal component analysis (PCA) of these EEG signals with a Multilayer Perception Neural Network (MLP) identifies an autistic subject and helps improve classification accuracy. We show differences between a working brain and a relaxed brain, especially in the Alpha waves used for diagnosis. © 2012 IEEE.},
note = {cited By 11},
keywords = {Biomedical Engineering, Brain, Brain Regions, Classification Accuracy, Diseases, EEG Signals, Electromagnetic Signals, Electromagnetics, Electromagnetism, Frequency Domains, International Group, Multilayer Perception Neural Networks, Neuroimaging, Principal Component Analysis},
pubstate = {published},
tppubtype = {conference}
}

@conference{KhazaalShams2011653,
title = {Characterizing autistic disorder based on principle component analysis},
author = {W Khazaal Shams and A W Abdul Rahman},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855644760&doi=10.1109%2fISIEA.2011.6108797&partnerID=40&md5=c486566e2d7ff404d830704c0b404067},
doi = {10.1109/ISIEA.2011.6108797},
isbn = {9781457714184},
year = {2011},
date = {2011-01-01},
journal = {2011 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2011},
pages = {653-657},
abstract = {Autism is often diagnosed during preschool or toddled age. This diagnosis often depends on behavioral test. It is known that individuals with autism have abnormal brain signals different from typical persons yet this difference in signals is slight that it is often difficult to distinguish from the normal. However, Electroencephalogram (EEG) signals have a lot of information which reflect the behavior of brain functions which therefore captures the marker for autism, help to early diagnose and speed the treatment. This work investigates and compares classification process for autism in open-eyed tasks and motor movement by using Principle Component Analysis (PCA) for feature extracted in Time-frequency domain to reduce data dimension. The results show that the proposed method gives accuracy in the range 90-100% for autism and normal children in motor task and around 90% to detect normal in open-eyed tasks though difficult to detect autism in this task. © 2011 IEEE.},
note = {cited By 6},
keywords = {Autism, Brain Function, Brain Signals, Classification Process, Data Dimensions, Diseases, Electroencephalogram Signals, Electroencephalography, Frequency Domain Analysis, Industrial Electronics, Motor Movements, Motor Tasks, PCA, Principal Component Analysis, Signal Detection, Time Frequency Domain},
pubstate = {published},
tppubtype = {conference}
}