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Neural Engineering Techniques for Autism Spectrum Disorder: Volume 1: Imaging and Signal Analysis de Ayman S. El-Baz

Neural Engineering Techniques for Autism Spectrum Disorder: Volume 1: Imaging and Signal Analysis

Editat de Ayman S. El-Baz, Jasjit S. Suri
en Limba Engleză Paperback – 20 iul 2021
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)
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Specificații

ISBN-13: 9780128228227
ISBN-10: 0128228229
Pagini: 400
Dimensiuni: 191 x 235 mm
Greutate: 0.69 kg
Editura: ELSEVIER SCIENCE

Cuprins

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