2012 |
Hoole, P R P; Pirapaharan, K; Basar, S A; Ismail, R; Liyanage, D L D A; Senanayake, S S H M U L; Hoole, S R H Autism, EEG and brain electromagnetics research Conference 2012, ISBN: 9781467316668, (cited By 11). Abstract | Links | BibTeX | Tags: 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 @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} } 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. |
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. |
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2012 |
Autism, EEG and brain electromagnetics research Conference 2012, ISBN: 9781467316668, (cited By 11). |
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). |
2010 |
Study of electroencephalography signal of autism and down syndrome children using FFT Conference 2010, ISBN: 9781424476473, (cited By 15). |