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. |
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2012 |
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). |