Neural Engineering Techniques for Autism Spectrum Disorder Books

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Neural Engineering Techniques for Autism Spectrum Disorder


Neural Engineering Techniques for Autism Spectrum Disorder
  • Author : Ayman S. El-Baz
  • Publisher : Academic Press
  • Release : 2021-07-20
  • ISBN : 9780128230657
  • Language : En, Es, Fr & De
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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. 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)

Handbook of Deep Learning in Biomedical Engineering


Handbook of Deep Learning in Biomedical Engineering
  • Author : Valentina Emilia Balas
  • Publisher : Academic Press
  • Release : 2020-11-23
  • ISBN : 9780128230473
  • Language : En, Es, Fr & De
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Deep learning (DL) is a method of machine learning, running over artificial neural networks, that uses multiple layers to extract high-level features from large amounts of raw data. DL methods apply levels of learning to transform input data into more abstract and composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the essential concepts of DL and its applications in the field of biomedical engineering. DL has been rapidly developed in recent years, in terms of both methodological constructs and practical applications. DL provides computational models of multiple processing layers to learn and represent data with higher levels of abstraction. It is able to implicitly capture intricate structures of large-scale data and is ideally suited to many of the hardware architectures that are currently available. The ever-expanding amount of data that can be gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and artificial intelligence techniques such as DL and convolutional neural networks to process and evaluate the data. Some examples of biomedical and clinical sensing devices that use DL include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic particle imaging, electroencephalography/magnetoencephalography (EE/MEG), optical microscopy and tomography, photoacoustic tomography, electron tomography, and atomic force microscopy. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications provides the most complete coverage of DL applications in biomedical engineering available, including detailed real-world applications in areas such as computational neuroscience, neuroimaging, data fusion, medical image processing, neurological disorder diagnosis for diseases such as Alzheimer’s, attention deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), tumor prediction, and translational multimodal imaging analysis. Presents a comprehensive handbook of the biomedical engineering applications of DL, including computational neuroscience, neuroimaging, time series data such as MRI, functional MRI, CT, EEG, MEG, and data fusion of biomedical imaging data from disparate sources, such as X-Ray/CT. Helps readers understand key concepts in DL applications for biomedical engineering and health care, including manifold learning, classification, clustering, and regression in neuroimaging data analysis. Provides readers with key DL development techniques such as creation of algorithms and application of DL through artificial neural networks and convolutional neural networks. Includes coverage of key application areas of DL such as early diagnosis of specific diseases such as Alzheimer’s, ADHD, and ASD, and tumor prediction through MRI and translational multimodality imaging and biomedical applications such as detection, diagnostic analysis, quantitative measurements, and image guidance of ultrasonography. ~

Neural Engineering


Neural Engineering
  • Author : Lijie Grace Zhang
  • Publisher : Springer
  • Release : 2016-06-29
  • ISBN : 9783319314334
  • Language : En, Es, Fr & De
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This book covers the principles of advanced 3D fabrication techniques, stem cells and biomaterials for neural engineering. Renowned contributors cover topics such as neural tissue regeneration, peripheral and central nervous system repair, brain-machine interfaces and in vitro nervous system modeling. Within these areas, focus remains on exciting and emerging technologies such as highly developed neuroprostheses and the communication channels between the brain and prostheses, enabling technologies that are beneficial for development of therapeutic interventions, advanced fabrication techniques such as 3D bioprinting, photolithography, microfluidics, and subtractive fabrication, and the engineering of implantable neural grafts.There is a strong focus on stem cells and 3D bioprinting technologies throughout the book, including working with embryonic, fetal, neonatal, and adult stem cells and a variety of sophisticated 3D bioprinting methods for neural engineering applications. There is also a strong focus on biomaterials, including various conductive biomaterials and biomimetic nanomaterials such as carbon-based nanomaterials and engineered 3D nanofibrous scaffolds for neural tissue regeneration. Finally, two chapters on in vitro nervous system models are also included, which cover this topic in the context of studying physiology and pathology of the human nervous system, and for use in drug discovery research. This is an essential book for biomedical engineers, neuroscientists, neurophysiologists, and industry professionals.

Education and Technology Support for Children and Young Adults With ASD and Learning Disabilities


Education and Technology Support for Children and Young Adults With ASD and Learning Disabilities
  • Author : Kats, Yefim
  • Publisher : IGI Global
  • Release : 2020-11-13
  • ISBN : 9781799870555
  • Language : En, Es, Fr & De
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Among the disabilities covered at the state and federal levels, autism and related conditions are a sharply growing diagnostic category among children and young adults. In education, administrators and practitioners working with affected learners are continually faced with confronting difficult problems such as getting adequate personnel training and choosing appropriate tools and techniques that best fit the specific needs of their students while at the same time satisfying their budget, technical resources, curriculum, and profile of the ASD population they serve. The choice of appropriate tools is especially complex due to the intrinsic connection between technical specifications, educational/therapeutic methods, and the wide variety of ASDs and related conditions. In this respect, tools chosen to support children may need to target those diagnosed not only with ASD but also with such co-morbidity conditions as attention deficit disorder. The instructional strategies and use of technology currently have room for improvement for online, hybrid, and face-to-face counseling settings. Also, an effective evaluation of educational technologies and tools would be fundamentally incomplete without a thorough understanding and assessment of the related special education practices as well as psychological and neurological issues specific for ASD and learning disabilities. Education and Technology Support for Children and Young Adults With ASD and Learning Disabilities provides an in-depth analysis on the use of available technology solutions, instructional design methods, and assessment techniques in the context of standards and regulations in classroom or counseling settings. The chapters contain theoretical analyses, vital practical information, and case studies that can function as guidelines for those involved in helping children and young adults with ASD or learning disabilities in online, hybrid, or face-to-face environments. While highlighting topics such as inclusive education, online gaming environments, assistive technologies, and cognitive development, this book is ideally intended for administrators, instructional technology specialists, special education faculty, counselors, instructional designers, course developers, social workers, and psychologists along with practitioners, stakeholders, researchers, and academicians interested in education and technology support for children and young adults with ASD and learning disabilities.

Computational Models for Biomedical Reasoning and Problem Solving


Computational Models for Biomedical Reasoning and Problem Solving
  • Author : Chen, Chung-Hao
  • Publisher : IGI Global
  • Release : 2019-04-12
  • ISBN : 9781522574682
  • Language : En, Es, Fr & De
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The results of computational model simulations allow researchers and clinicians to make predictions about what will happen in the biological systems that are being studied in response to changing conditions for a disease or disorder. With a well-developed computational model, researchers and clinicians can better understand the cause of a disease or a disorder and predict treatment results. Computational Models for Biomedical Reasoning and Problem Solving is a critical scholarly publication that provides insightful strategies to developing computational models that allow for the better understanding and treatment of various diseases and disorders. Featuring topics such as biomedicine, neuroscience, and artificial intelligence, this book is ideal for practitioners, clinicians, researchers, psychologists, and engineers.