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Neural Engineering Techniques for Autism Spectrum Disorder
Volume 1: Imaging and Signal Analysis
- 1st Edition - July 16, 2021
- Editors: Ayman S. El-Baz, Jasjit S. Suri
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 2 8 2 2 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 0 6 5 - 7
Neural Engineering for Autism Spectrum Disorder, Volume One: Imaging and Signal Analysis Techniques presents the latest advances in neural engineering and biomedical engine… Read more
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Request a sales quoteNeural 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.
- 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)
Biomedical Engineers and researchers in neural engineering, medical imaging, and neural networks. Students, researchers and clinicians in autism, and a variety of other specialties
- Cover
- Title page
- Contents
- Copyright
- Dedication
- Contributors
- Biography of the editors
- Acknowledgments
- Chapter 1: Prediction of outcome in children with autism spectrum disorders
- Abstract
- 1. Introduction
- 2. Screening data studies
- 3. Diagnostic test data studies
- 4. EEG and ERP data studies
- 5. Eye-tracking data studies
- 6. MRI data studies
- 7. Conclusions
- Chapter 2: Autism spectrum disorder and sleep: pharmacology management
- Abstract
- 1. Autism spectrum disorder
- 2. Sleep comorbid conditions in ASD
- 3. Pharmacological treatment of sleep comorbid conditions in ASD
- 4. Conclusions
- Chapter 3: Diagnosis of autism spectrum disorder with convolutional autoencoder and structural MRI images
- Abstract
- Abbreviations
- 1. Introduction
- 2. Material and methods
- 3. Results and discussion
- 4. Conclusion
- Chapter 4: Explainable and scalable machine learning algorithms for detection of autism spectrum disorder using fMRI data
- Abstract
- 1. Introduction
- 2. Current diagnostic practices
- 3. Neuroimaging data sets for biomarker detection of Autism Spectrum Disorder
- 4. ASD-DiagNET: deep-learning model for fMRI based ASD classification
- 5. Future work: Integration of mutliple modalities for greater classification accuracy, and generalizability
- 6. Discussion and Conclusions
- Chapter 5: Smart architectures for evaluating the autonomy and behaviors of people with autism spectrum disorder in smart homes
- Abstract
- 1. Introduction
- 2. Sensors and methodologies for collecting stereotypical motor movements and gestures in people with ASD
- 3. Smart ambient devices for collecting human daily activities of people with ASD
- 4. Architectures: persistence, distribution, and exploration in sensing HAR
- 5. Conclusions and ongoing works
- Chapter 6: Data mining and machine learning techniques for early detection in autism spectrum disorder
- Abstract
- 1. Introduction
- 2. State of the art
- 3. Methodology
- 4. Experimentation
- 5. Conclusion
- Chapter 7: Altered gut–brain signaling in autism spectrum disorders—from biomarkers to possible intervention strategies
- Abstract
- 1. Introduction
- 2. Microbiota profiles in ASD
- 3. A systematic review of rodent and human studies
- 4. Discussion
- 5. Conclusion
- Chapter 8: Machine learning methods for autism spectrum disorder classification
- Abstract
- 1. Introduction
- 2. Mathematical preliminaries
- 3. ASD classification using graph kernels
- 4. ASD classification using bootstrapped graph neural networks
- 5. Conclusions
- Acknowledgment
- Disclaimer
- Chapter 9: Exploring tree-based machine learning methods to predict autism spectrum disorder
- Abstract
- 1. Introduction
- 2. Theoretical background and related works
- 3. Research methodology
- 4. Data collection and analysis
- 5. Developing predictive algorithm
- 6. Evaluating predictive models
- 7. Discussion and conclusion
- Chapter 10: Blood serum–infrared spectra-based chemometric models for auxiliary diagnosis of autism spectrum disorder
- Abstract
- 1. Introduction
- 2. Fundamentals of infrared spectroscopy
- 3. Multivariate statistics clustering and classification methods
- 4. Case study: blood serum IR spectra–based models for ASD complementary diagnosis in children and adolescents
- 5. Conclusion
- Acknowledgments
- Chapter 11: A deep learning predictive classifier for autism screening and diagnosis
- Abstract
- 1. Introduction
- 2. Related work
- 3. Deep representation learning for autism screening
- 4. Experimental results
- 5. Discussion
- 6. Conclusion
- Chapter 12: Diagnosis of autism spectrum disorder by causal influence strength learned from resting-state fMRI data
- Abstract
- 1. Introduction
- 2. Background knowledge and motivation
- 3. The two-step method for cyclic and large-scale causal discovery
- 4. Identifiability of causal model with two-step method
- 5. Autism spectrum disorder diagnosis on ABIDE dataset
- 6. Discussions
- 7. Conclusions and future work
- Acknowledgements
- Chapter 13: Adapting multisystemic therapy to the treatment of disruptive behavior problems in youths with autism spectrum disorder: toward improving the practice of health care
- Abstract
- 1. Introduction
- 2. Correlates of disruptive behavior problems in autistic youths
- 3. Multisystemic therapy for youths with autism spectrum disorder (MST-ASD)
- 4. Development and evaluation of MST-ASD
- 5. Future directions in treatment for disruptive behaviors among youths with ASD
- Chapter 14: Machine learning–based patient-specific processor for the early intervention in autistic children through emotion detection
- Abstract
- 1. Introduction
- 2. Background and related work
- 3. Human emotions classification processor algorithmic implementation
- 4. Proposed ML-based emotion processor
- 5. Measurement results and performance
- 6. Conclusion
- Acknowledgment
- Chapter 15: Autism spectrum disorders and anxiety: measurement and treatment
- Abstract
- 1. Introduction
- 2. Autism spectrum disorder and anxiety
- 3. Screening and diagnosis of anxiety in ASD
- 4. Intervention programs
- 5. Technological development and development of CBT
- 6. Conclusion
- Chapter 16: Extract image markers of autism using hierarchical feature selection technique
- Abstract
- 1. Introduction
- 2. Materials and methods
- 3. Results and discussion
- 4. Conclusion
- Chapter 17: Early autism analysis and diagnosis system using task-based fMRI in a response to speech task
- Abstract
- 1. Introduction
- 2. Materials
- 3. Methodology
- 4. Experimental results
- 5. Conclusion and future work
- Chapter 18: Identifying brain pathological abnormalities of autism for classification using diffusion tensor imaging
- Abstract
- 1. Introduction
- 2. Methodology
- 3. Experimental results
- 4. Conclusion and discussion
- Index
- No. of pages: 400
- Language: English
- Edition: 1
- Published: July 16, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128228227
- eBook ISBN: 9780128230657
AS
Ayman S. El-Baz
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).
Affiliations and expertise
University of Louisville, USAJS
Jasjit S. Suri
Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.
Affiliations and expertise
Chairman, AtheroPoint LLC, USARead Neural Engineering Techniques for Autism Spectrum Disorder on ScienceDirect