List of Publications
There are numbers of autism related research can be found in Malaysia that generally focus on the ASD, learning disorder, communication aids, therapy and many more. The list of publications is provided below:
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2018 |
Sudirman, R; Hussin, S S; Airij, A G; Hai, C Z Profile indicator for autistic children using EEG biosignal potential of sensory tasks Conference Institute of Electrical and Electronics Engineers Inc., 2018, ISBN: 9781538612774, (cited By 0). Abstract | Links | BibTeX | Tags: Autistic Children, Biomedical Signal Processing, Brain, Children with Autism, Electroencephalography, Electrophysiology, Entropy Approximations, Independent Component Analysis, MATLAB, Neural Networks, Neurological Problems, Sensory Analysis, Sensory Profiles, Sensory Stimulation, Wavelet Packet Transforms @conference{Sudirman2018136, title = {Profile indicator for autistic children using EEG biosignal potential of sensory tasks}, author = {R Sudirman and S S Hussin and A G Airij and C Z Hai}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058032461&doi=10.1109%2fICBAPS.2018.8527403&partnerID=40&md5=30dbb1596f4a0529332713c087bd788d}, doi = {10.1109/ICBAPS.2018.8527403}, isbn = {9781538612774}, year = {2018}, date = {2018-01-01}, journal = {2nd International Conference on BioSignal Analysis, Processing and Systems, ICBAPS 2018}, pages = {136-141}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Electroencephalography (EEG) is a measure of voltages caused due to neural activities within the brain. EEG is a recommended tool for diagnosing neurological problems because it is non-invasive and can be recorded over a longer time-period. The children with Autism Spectrum Disorder (ASD) have difficulty in expressing their emotions due to their inability of proper information processing in brain. Therefore, this research aims to build a sensory profile with the help of EEG biosignal potential to distinguish among different sensory responses. The EEG signals acquired in this research identify different emotional states such as positive-thinking or super-learning and light-relaxation and are within the frequency range of 8-12 Hertz. 64 children participated in this research among which 34 were children with ASD and 30 were normal children. The EEG data was recoded while all the children were provided with vestibular, visual, sound, taste and vestibular sensory stimulations. The raw EEG data was filtered with the help of independent component analysis (ICA) using wavelet transform and EEGLAB software. Later, for building the sensory profile, entropy approximation, means and standard deviations were extracted from the filtered EEG signals. Along with that, the filtered EEG data was also fed to a neural networks (NN) algorithm which was implemented in MATLAB. Results from the acquired EEG signals depicted that during the sensory stimulation phase, the responses of all autistic children were in an unstable state. These findings will equip and aid their learning strategy in the future. © 2018 IEEE.}, note = {cited By 0}, keywords = {Autistic Children, Biomedical Signal Processing, Brain, Children with Autism, Electroencephalography, Electrophysiology, Entropy Approximations, Independent Component Analysis, MATLAB, Neural Networks, Neurological Problems, Sensory Analysis, Sensory Profiles, Sensory Stimulation, Wavelet Packet Transforms}, pubstate = {published}, tppubtype = {conference} } Electroencephalography (EEG) is a measure of voltages caused due to neural activities within the brain. EEG is a recommended tool for diagnosing neurological problems because it is non-invasive and can be recorded over a longer time-period. The children with Autism Spectrum Disorder (ASD) have difficulty in expressing their emotions due to their inability of proper information processing in brain. Therefore, this research aims to build a sensory profile with the help of EEG biosignal potential to distinguish among different sensory responses. The EEG signals acquired in this research identify different emotional states such as positive-thinking or super-learning and light-relaxation and are within the frequency range of 8-12 Hertz. 64 children participated in this research among which 34 were children with ASD and 30 were normal children. The EEG data was recoded while all the children were provided with vestibular, visual, sound, taste and vestibular sensory stimulations. The raw EEG data was filtered with the help of independent component analysis (ICA) using wavelet transform and EEGLAB software. Later, for building the sensory profile, entropy approximation, means and standard deviations were extracted from the filtered EEG signals. Along with that, the filtered EEG data was also fed to a neural networks (NN) algorithm which was implemented in MATLAB. Results from the acquired EEG signals depicted that during the sensory stimulation phase, the responses of all autistic children were in an unstable state. These findings will equip and aid their learning strategy in the future. © 2018 IEEE. |
2014 |
Sudirman, R; Hussin, S S Sensory responses of autism via electroencephalography for Sensory Profile Conference Institute of Electrical and Electronics Engineers Inc., 2014, ISBN: 9781479956869, (cited By 3). Abstract | Links | BibTeX | Tags: Autism, Discrete Wavelet Transforms, Diseases, Electroencephalography, Electrophysiology, Independent Component Analysis, International System, Learning, Sensory Analysis, Sensory Profiles, Sensory Profiling, Sensory Stimulation, Signal Processing, Standard Deviation, Wavelet Packet Transforms @conference{Sudirman2014626, title = {Sensory responses of autism via electroencephalography for Sensory Profile}, author = {R Sudirman and S S Hussin}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946435600&doi=10.1109%2fICCSCE.2014.7072794&partnerID=40&md5=3e6f1cfe19eae4fad359d2493aebd7e0}, doi = {10.1109/ICCSCE.2014.7072794}, isbn = {9781479956869}, year = {2014}, date = {2014-01-01}, journal = {Proceedings - 4th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2014}, pages = {626-631}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {The aim of this study is to investigate the brain signals of autism children through electroencephalography (EEG) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, taste, touch, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE.}, note = {cited By 3}, keywords = {Autism, Discrete Wavelet Transforms, Diseases, Electroencephalography, Electrophysiology, Independent Component Analysis, International System, Learning, Sensory Analysis, Sensory Profiles, Sensory Profiling, Sensory Stimulation, Signal Processing, Standard Deviation, Wavelet Packet Transforms}, pubstate = {published}, tppubtype = {conference} } The aim of this study is to investigate the brain signals of autism children through electroencephalography (EEG) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, taste, touch, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE. |
2012 |
Shams, W K; Wahab, A; Qidwai, U A Fuzzy model for detection and estimation of the degree of autism spectrum disorder Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7666 LNCS (PART 4), pp. 372-379, 2012, ISSN: 03029743, (cited By 2). Abstract | Links | BibTeX | Tags: Autism Spectrum Disorders, Classification (of information), Data Processing, Detection and Estimation, Diseases, Early Intervention, EEG Signals, Electrophysiology, Fuzzy Approach, Fuzzy Modeling, Spectrum Energy, Subtractive Clustering, Time-Frequency Transformation, Treatment Process @article{Shams2012372, title = {Fuzzy model for detection and estimation of the degree of autism spectrum disorder}, author = {W K Shams and A Wahab and U A Qidwai}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869038189&doi=10.1007%2f978-3-642-34478-7_46&partnerID=40&md5=98929aba468010a02f652994b0da2a54}, doi = {10.1007/978-3-642-34478-7_46}, issn = {03029743}, year = {2012}, date = {2012-01-01}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {7666 LNCS}, number = {PART 4}, pages = {372-379}, abstract = {Early detection of autism spectrum disorder (ASD) is of great significance for early intervention. Besides, knowing the degree of severity in ASD and how it changes with the intervention is imperative for the treatment process. This study proposes Takagi- Sugeno-Kang (TSK) fuzzy modeling approach that is based on subtractive clustering to classify autism spectrum disorder and to estimate the degree of prognosis. The study has been carried out using Electroencephalography (EEG) signal on two groups of control and ASD children age-matched between seven to nine years old. EEG signals are quantized to temporal-time domain using Short Time Frequency Transformation (STFT). Spectrum energy is extracted as features for alpha band. The proposed system is modeled to estimate the degree in which subject is autistic, normal or uncertain. The results show accuracy in range (70-97) % when using fuzzy model .Also this system is modeled to generate crisp decision; the results show accuracy in the range (80-100) %. The proposed model can be adapted to help psychiatrist for diagnosis and intervention process. © 2012 Springer-Verlag.}, note = {cited By 2}, keywords = {Autism Spectrum Disorders, Classification (of information), Data Processing, Detection and Estimation, Diseases, Early Intervention, EEG Signals, Electrophysiology, Fuzzy Approach, Fuzzy Modeling, Spectrum Energy, Subtractive Clustering, Time-Frequency Transformation, Treatment Process}, pubstate = {published}, tppubtype = {article} } Early detection of autism spectrum disorder (ASD) is of great significance for early intervention. Besides, knowing the degree of severity in ASD and how it changes with the intervention is imperative for the treatment process. This study proposes Takagi- Sugeno-Kang (TSK) fuzzy modeling approach that is based on subtractive clustering to classify autism spectrum disorder and to estimate the degree of prognosis. The study has been carried out using Electroencephalography (EEG) signal on two groups of control and ASD children age-matched between seven to nine years old. EEG signals are quantized to temporal-time domain using Short Time Frequency Transformation (STFT). Spectrum energy is extracted as features for alpha band. The proposed system is modeled to estimate the degree in which subject is autistic, normal or uncertain. The results show accuracy in range (70-97) % when using fuzzy model .Also this system is modeled to generate crisp decision; the results show accuracy in the range (80-100) %. The proposed model can be adapted to help psychiatrist for diagnosis and intervention process. © 2012 Springer-Verlag. |
2010 |
Sudirman, ; Saidin, S; Safri, Mat N Study of electroencephalography signal of autism and down syndrome children using FFT Conference 2010, ISBN: 9781424476473, (cited By 15). Abstract | Links | BibTeX | Tags: Alpha Value, Autism, Down Syndrome, EEG Signals, Electroencephalography, Electrophysiology, Fast Fourier Transforms, Industrial Electronics, Metadata, User Interfaces, Visual Evoked Potential, Visualization @conference{Sudirman2010401, title = {Study of electroencephalography signal of autism and down syndrome children using FFT}, author = {Sudirman and S Saidin and N Mat Safri}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251542066&doi=10.1109%2fISIEA.2010.5679434&partnerID=40&md5=17fce4f69b27a3cc644f36c118b6ec6e}, doi = {10.1109/ISIEA.2010.5679434}, isbn = {9781424476473}, year = {2010}, date = {2010-01-01}, journal = {ISIEA 2010 - 2010 IEEE Symposium on Industrial Electronics and Applications}, pages = {401-406}, abstract = {Electroencephalography (EEG) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal was recorded and captured from normal and special children based on their visual response using Visual Evoked Potential (VEP) method. The data is analyzed using Fast Fourier Transform (FFT), so that, normal and special children can be distinguished based on alpha (α) value. As a result, alpha value for normal children at 10 Hz is higher than autism and Down syndrome children. A friendly user interface was built for easy storage and visualization. ©2010 IEEE.}, note = {cited By 15}, keywords = {Alpha Value, Autism, Down Syndrome, EEG Signals, Electroencephalography, Electrophysiology, Fast Fourier Transforms, Industrial Electronics, Metadata, User Interfaces, Visual Evoked Potential, Visualization}, pubstate = {published}, tppubtype = {conference} } Electroencephalography (EEG) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal was recorded and captured from normal and special children based on their visual response using Visual Evoked Potential (VEP) method. The data is analyzed using Fast Fourier Transform (FFT), so that, normal and special children can be distinguished based on alpha (α) value. As a result, alpha value for normal children at 10 Hz is higher than autism and Down syndrome children. A friendly user interface was built for easy storage and visualization. ©2010 IEEE. |