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The book explores the prevalence of ASD and the challenges associated with its early detection. Recognizing the limitations of existing diagnostic methods, the volume emphasizes the need for a multidisciplinary approach, utilizing the collective strengths of artificial intelligence (AI), biomedical engineering, and applied neuroscience. This convergence promises not only to enhance diagnostic accuracy but also to streamline the process, facilitating timely interventions for improved treatment.
Key Features:
- Illustrates the latest advancements in AI, biomedical engineering, and applied neuroscience, providing readers with a comprehensive overview of cutting-edge technologies in autism detection.
- Integrates diverse perspectives from leading experts, merging the fields of AI, biomedical engineering, and neuroscience to present a unified and multidisciplinary approach to autism diagnosis.
- Demonstrates the practical applications of innovative diagnostic tools, from machine learning algorithms to biomedical devices, offering real-world insights and case studies for effective implementation.
- Explores the future directions of autism detection, discussing emerging technologies and ethical considerations.
- Guides readers through a journey of discovery, unraveling the complexities of autism spectrum disorders, and empowering healthcare professionals, researchers, and students with actionable knowledge for enhanced diagnosis and support.
Inhaltsverzeichnis
Preface. 1. Introductory Concepts of Neuroscience and Computational Intelligence. 2. Autism Spectrum Disorder: Overview and the Historical Path of Diagnosis. 3. Prevailing Diagnostic Scenario and the Importance of Early Detection. 4. Autism and the DIR/Floortime Model: A Counterpart to Approaches That do not Address Human Singularity. 5. Advanced Strategies for ASD Detection: A Narrative Review. 6. Enhancing EEG-Based ASD Detection Using Wavelet Transforms and Hybrid Deep Learning Models. 7. Advances in Differential Diagnosis of ASD: EEG Electrode Reduction with Machine Learning. 8. Innovations and Emerging Technologies in the Context of Autism Spectrum Disorders (ASD). 9. Unknown Intelligences: Artificial Intelligence, Autism, and the New Paths of Ethical Care.
Über den Autor / die Autorin
Wellington Pinheiro dos Santos is the Head of the Department of Biomedical Engineering at the Federal University of Pernambuco (UFPE) and the Coordinator of the Biomedical Computing Laboratory at UFPE. He holds a Bachelor's degree in Electrical and Electronic Engineering (2001) and a Master's degree in Electrical Engineering (2003) from UFPE, and earned his PhD in Electrical Engineering from the Federal University of Campina Grande in 2009. At UFPE, Dr. dos Santos is actively involved in both undergraduate and graduate programs in Biomedical Engineering. Since 2009, he has also been a member of the Graduate Program in Computer Engineering at the Escola Politécnica de Pernambuco, Universidade de Pernambuco. Additionally, he is a Researcher at the Institute of the Economic-Industrial Complex (ICEIS), where he coordinates the Biomedical Computing and Bioengineering axes. He is a member of the Brazilian Society of Biomedical Engineering (SBEB), the Brazilian Society of Computational Intelligence (SBIC), the Brazilian Society for Health Informatics (SBIS), and the International Federation of Medical and Biological Engineering (IFMBE).
Flávio Secco Fonsêca earned his degree in Mechatronics Engineering from the University of Pernambuco in 2017. Currently, he works as a faculty member in the Analysis and Systems Development courses at the Universidade Tiradentes (UNIT). Previously, he was a professor at the Center for Education for Vocational Education (CEPEP). With a focus on Mechatronics and the steel industry, he has experience in Mechanical Engineering. He holds a master's degree in Computer Engineering from the University of Pernambuco (POLI/UPE), conducting research in emotion recognition through voice signals and affective computing. Currently pursuing a Ph.D. at the same institution, he is also part of the Biomedical Computing research group at the Federal University of Pernambuco (UFPE), conducting studies on autism and machine learning. His interests extend to the development of digital games and research in the fields of Educational Technologies, Artificial Intelligence, Health, and Design.
Juliana Carneiro Gomes is a Biomedical Engineer from the Federal University of Pernambuco (UFPE-2016) with a sandwich period under the Science Without Borders program (CAPES) in the United States. During this time, she completed an academic year at Mercer University and worked as a researcher at the Advanced Imaging Algorithms and Instrumentation Laboratory (AIAI Lab) at Johns Hopkins University School of Medicine, gaining experience in Computed Tomography (CT) Image Processing. She holds a Master's degree in Biomedical Engineering from CTG/UFPE (2019), focusing on Electrical Impedance Tomography (EIT) image reconstruction using Artificial Neural Networks. Currently a Ph.D. holder in Computer Engineering at the University of Pernambuco (UPE) and a member of the Biomedical Computing Research Group - UFPE, her research emphasizes applied Neuroscience. Juliana has also served as a substitute professor in the Physics Department at UFPE and was a student representative on the National Commission for Higher Education Assessment (CONAES). Currently, Juliana is a postdoctoral researcher at the Department of Biomedical Engineering (UFPE), a temporary faculty member of the Graduate Program in Biomedical Engineering (UFPE), and a faculty member in the Specialization in Data Science and Digital Health (UFPE).
Dr. Maíra Araújo de Santana is a biomedical engineer specializing in artificial intelligence applications in healthcare. She earned her Bachelor's degree in Biomedical Engineering from the Federal University of Pernambuco (UFPE), Brazil, in 2017. During her undergraduate studies, she spent an academic year at the University of Alabama at Birmingham in the United States and worked as a researcher at Duke University's Carl E. Ravin Advanced Imaging Laboratories (RAI Labs), where she developed expertise in medical image processing. Dr. Santana holds a Master's degree in Biomedical Engineering (2020) and a PhD in Computer Engineering (2023) from UFPE. She is currently a postdoctoral researcher and a faculty member in the Specialization in Data Science and Digital Health at the Department of Biomedical Engineering at UFPE. She is a member of the Biomedical Computing Research Group at UFPE, focusing her research on artificial intelligence applied to health. Her work encompasses areas such as pattern recognition for early diagnosis of breast cancer, affective computing, and applied neuroscience.
