Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation.
The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.
1. Artificial Intelligence for Neurological Disorders - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. Contributors - p. xvii 8. About the editors - p. xix 9. Preface - p. xxiii 10. Acknowledgment - p. xxix 11. Chapter 1 Early detection of neurological diseases using machine learning and deep learning techniques: A review - p. 1 12. Chapter 2 A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset - p. 25 13. Chapter 3 Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proliferation in human brain - p. 49 14. Chapter 4 Convolutional neural network model for identifying neurological visual disorder - p. 61 15. Chapter 5 Recurrent neural network model for identifying neurological auditory disorder - p. 77 16. Chapter 6 Recurrent neural network model for identifying epilepsy based neurological auditory disorder - p. 91 17. Chapter 7 Dementia diagnosis with EEG using machine learning - p. 107 18. Chapter 8 Computational methods for translational brain-behavior analysis - p. 131 19. Chapter 9 Clinical applications of deep learning in neurology and its enhancements with future directions - p. 145 20. Chapter 10 Ensemble sparse intelligent mining techniques for cognitive disease - p. 159 21. Chapter 11 Cognitive therapy for brain diseases using deep learning models - p. 171 22. Chapter 12 Cognitive therapy for brain diseases using artificial intelligence models - p. 185 23. Chapter 13 Clinical applications of deep learning in neurology and its enhancements with future predictions - p. 209 24. Chapter 14 An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning - p. 225 25. Chapter 15 Neural signaling and communication using machine learning - p. 245 26. Chapter 16 Classification of neurodegenerative disorders using machine learning techniques - p. 261 27. Chapter 17 New trends in deep learning for neuroimaging analysis and disease prediction - p. 275 28. Chapter 18 Prevention and diagnosis of neurodegenerative diseases using machine learning models - p. 289 29. Chapter 19 Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis - p. 305 30. Chapter 20 An insight into applications of deep learning in neuroimaging - p. 323 31. Chapter 21 Incremental variance learning-based ensemble classification model for neurological disorders - p. 341 32. Chapter 22 A systematic review of adaptive machine learning techniques for early detection of Parkinson's disease - p. 361 33. Index - p. 387
Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation.
The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.
1. Artificial Intelligence for Neurological Disorders - p. i 2. Cover - p. i 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Dedication - p. v 7. Contributors - p. xvii 8. About the editors - p. xix 9. Preface - p. xxiii 10. Acknowledgment - p. xxix 11. Chapter 1 Early detection of neurological diseases using machine learning and deep learning techniques: A review - p. 1 12. Chapter 2 A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset - p. 25 13. Chapter 3 Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proliferation in human brain - p. 49 14. Chapter 4 Convolutional neural network model for identifying neurological visual disorder - p. 61 15. Chapter 5 Recurrent neural network model for identifying neurological auditory disorder - p. 77 16. Chapter 6 Recurrent neural network model for identifying epilepsy based neurological auditory disorder - p. 91 17. Chapter 7 Dementia diagnosis with EEG using machine learning - p. 107 18. Chapter 8 Computational methods for translational brain-behavior analysis - p. 131 19. Chapter 9 Clinical applications of deep learning in neurology and its enhancements with future directions - p. 145 20. Chapter 10 Ensemble sparse intelligent mining techniques for cognitive disease - p. 159 21. Chapter 11 Cognitive therapy for brain diseases using deep learning models - p. 171 22. Chapter 12 Cognitive therapy for brain diseases using artificial intelligence models - p. 185 23. Chapter 13 Clinical applications of deep learning in neurology and its enhancements with future predictions - p. 209 24. Chapter 14 An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning - p. 225 25. Chapter 15 Neural signaling and communication using machine learning - p. 245 26. Chapter 16 Classification of neurodegenerative disorders using machine learning techniques - p. 261 27. Chapter 17 New trends in deep learning for neuroimaging analysis and disease prediction - p. 275 28. Chapter 18 Prevention and diagnosis of neurodegenerative diseases using machine learning models - p. 289 29. Chapter 19 Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis - p. 305 30. Chapter 20 An insight into applications of deep learning in neuroimaging - p. 323 31. Chapter 21 Incremental variance learning-based ensemble classification model for neurological disorders - p. 341 32. Chapter 22 A systematic review of adaptive machine learning techniques for early detection of Parkinson's disease - p. 361 33. Index - p. 387
