Neural Engineering for Autism Spectrum Disorder, Volume One: Imaging and Signal Analysis Techniques presents the latest advances in neural engineering and biomedical engineering as applied to the clinical diagnosis and treatment of Autism Spectrum Disorder (ASD). Advances in the role of neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, social behaviors and suitable data analytics useful for clinical diagnosis and research applications for Autism Spectrum Disorder are covered, including relevant case studies. The application of brain signal evaluation, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals are presented for detection and estimation of the degree of ASD.
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1. Prediction of outcome in children with autism spectrum disorders2. Autism spectrum disorder and sleep: pharmacology management3. Diagnosis of autism spectrum disorder with convolutional autoencoder and structural MRI images4. Explainable and scalable machine learning algorithms for detection of autism spectrum disorder using fMRI data5. Smart architectures for evaluating the autonomy and behaviors of people with autism spectrum disorder in smart homes6. Data mining and machine learning techniques for early detection in autism spectrum disorder7. Altered gut–brain signaling in autism spectrum disorders—from biomarkers to possible intervention strategies8. Machine learning methods for autism spectrum disorder classification9.Exploring tree-based machine learning methods to predict autism spectrum disorder10. Blood serum–infrared spectra-based chemometric models for auxiliary diagnosis of autism spectrum disorder11. A deep learning predictive classifier for autism screening and diagnosis12. Diagnosis of autism spectrum disorder by causal influence strength learned from resting-state fMRI data13. Adapting multisystemic therapy to the treatment of disruptive behavior problems in youths with autism spectrum disorder: toward improving the practice of health care14. Machine learning–based patient-specific processor for the early intervention in autistic children through emotion detection15. Autism spectrum disorders and anxiety: measurement and treatment16. Extract image markers of autism using hierarchical feature selection technique17. Early autism analysis and diagnosis system using task-based fMRI in a response to speech task18. Identifying brain pathological abnormalities of autism for classification using diffusion tensor imaging
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Examines the latest applications of Neural Networks and other Machine Learning techniques applied to Autism Spectrum Disorder
Presents applications of Neural Engineering and other Machine Learning techniques for the diagnosis of Autism Spectrum Disorder (ASD) Includes in-depth technical coverage of imaging and signal analysis techniques, including coverage of functional MRI, neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, and neuroanatomy of autism Covers Signal Analysis for the detection and estimation of Autism Spectrum Disorder (ASD), including brain signal analysis, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals for ASD Written to help engineers, computer scientists, researchers and clinicians understand the technology and applications of Neural Engineering for the detection and diagnosis of Autism Spectrum Disorder (ASD)
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Produktdetaljer

ISBN
9780128228227
Publisert
2021-07-20
Utgiver
Vendor
Academic Press Inc
Vekt
450 gr
Høyde
235 mm
Bredde
191 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
400

Redaktør

Biographical note

Dr. El-Baz is a Professor, University Scholar, and Chair of the Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master’s degrees in Electrical Engineering in 1997 and 2001, respectively. He earned his doctoral degree in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contributions to the field of biomedical translational research. Dr. El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 450 technical articles (105 journals, 15 books, 50 book chapters, 175 refereed-conference papers, 100 abstracts, and 15 US patents). Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General’s Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.