Artificial Intelligence for Neurological Disorders
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,...
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| Main Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Chantilly
Elsevier Science & Technology
2022
Academic Press |
| Edition: | 1 |
| Subjects: | |
| ISBN: | 0323902774, 9780323902779 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Pani, Subhendu Kumar Dash, Sujata García-Hernández, Laura Abraham, Ajith |
| Author_xml | – sequence: 1 fullname: Abraham, Ajith – sequence: 2 fullname: Dash, Sujata – sequence: 3 fullname: Pani, Subhendu Kumar – sequence: 4 fullname: García-Hernández, Laura |
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| ContentType | eBook |
| DEWEY | 616.80028563 |
| DOI | 10.1016/C2020-0-03449-3 |
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| Discipline | Medicine |
| EISBN | 9780323902786 0323902782 |
| Edition | 1 |
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| PageCount | 434 |
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| PublicationDate | 2022 2022-09-30 |
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| PublicationDecade | 2020 |
| PublicationPlace | Chantilly |
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| PublicationYear | 2022 |
| Publisher | Elsevier Science & Technology Academic Press |
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| Snippet | Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine... |
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| SubjectTerms | Artificial intelligence Nervous system Nervous system-Diseases-Treatment |
| TableOfContents | Chapter 12: Cognitive therapy for brain diseases using artificial intelligence models -- Introduction -- Brain diseases -- Brain diseases and physiological signals -- Artificial intelligence -- Artificial intelligence, neuroscience, and clinical practice -- Data acquisition and image interpretation -- Artificial intelligence and cognitive behavioral therapy -- Challenges and pitfalls -- Summary -- Conclusion and future direction -- References -- Chapter 13: Clinical applications of deep learning in neurology and its enhancements with future predictions -- Introduction -- Neural network systems, biomarkers, and physiological signals -- Neurological techniques, biomedical informatics, and computational neurophysiology -- Neurological techniques -- Biomedical informatics -- Computational neurophysiology -- Data and image acquisition -- Artificial intelligence and deep learning -- Artificial intelligence and neurological disease prediction -- Non-clinical health-related applications -- Challenges and potential pitfalls of neurological techniques -- Conclusion and future directions -- References -- Chapter 14: An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning -- Introduction -- Epileptic seizure -- Seizure localization -- Physiological and pathophysiological signals -- Chemical signals as physiological signals -- Endocrine disorders as deviations from physiological signals -- Neurotransmitter detection using artificial intelligence -- Electrical signals as physiological signals -- Action potentials -- Application of electrical signals -- Artificial intelligence and action potential detection -- Electrocorticography and electroencephalography -- Electroencephalography -- Electrocardiograph recording and placement -- Electroencephalography and other non-invasive techniques Cognitive mapping and neural coding -- Neuroelectrophysiology modeling -- Clinical translation of cognitive mapping and neural coding -- Systems biology in translational and computational biology -- Application of system biology in translational brain tumor research -- Summary -- Conclusion -- References -- Chapter 9: Clinical applications of deep learning in neurology and its enhancements with future directions -- Introduction -- Medical data and artificial intelligence in neurology -- Neurology-centered medical system -- Clinical applications of artificial intelligence and deep learning -- Artificial intelligence for medical imaging and precision medicine -- Examples of neurology AI -- Challenges of deep learning applied to neuroimaging techniques -- AI for assessing response to targeted neurological therapies -- Conclusion and future perspectives -- References -- Chapter 10: Ensemble sparse intelligent mining techniques for cognitive disease -- Introduction -- Cognitive disease -- Machine learning and deep ensemble sparse regression network -- Intelligent medical diagnostics with ensemble sparse intelligent mining techniques -- High-dimensional data science in cognitive diseases -- Diagnostic challenges with artificial intelligence -- Summary -- Conclusion and future perspectives -- References -- Chapter 11: Cognitive therapy for brain diseases using deep learning models -- Introduction -- Brain diseases affecting cognitive functions -- Multimodal information -- Connectome mapping -- Post-operative seizure -- Gene signature -- Overview of deep learning techniques -- Data preprocessing techniques -- Early brain disease diagnosis using deep learning techniques -- Artificial intelligence and cognitive therapies and immunotherapies -- Summary -- Conclusion and future perspectives -- References Applications of electroencephalography -- Electrocorticography -- Role of data scientists in epileptic seizure detection -- Intelligent diagnostic approaches: Machine learning and deep learning -- Selecting appropriate machine learning classifiers and features -- Summary -- Conclusion and future research -- References -- Chapter 15: Neural signaling and communication using machine learning -- Introduction -- Electrophysiology of brain waves -- Electrophysiology of alpha waves -- Electrophysiology of beta waves -- Electrophysiology of delta waves -- Electrophysiology of theta waves -- Electrophysiology of gamma waves -- Electrophysiology of mu waves -- Electrophysiology of sensorimotor rhythms -- Neural signaling and communication -- Neural signaling and communication -- Electrical signals as physiological signals -- Action potentials -- Application of electrical signals -- Brain-computer interface (data acquisition) -- Algorithm classification of brain functions using machine learning -- Artificial intelligence and neural signals, communications -- Challenges and opportunities -- Summary -- Conclusion and future perspectives -- References -- Chapter 16: Classification of neurodegenerative disorders using machine learning techniques -- Introduction -- Patient datasets -- Related medical examinations -- Clinical tests -- Biomarkers -- Clinical tests and biomarkers -- Classification of neurodegenerative diseases -- Machine learning techniques as computer-assisted diagnostic systems -- Multimodal analysis -- Conclusion and future perspectives -- References -- Chapter 17: New trends in deep learning for neuroimaging analysis and disease prediction -- Introduction -- Deep learning techniques -- Neuroimaging and data science -- Cognitive impairment -- Images, text, sounds, waves, biomarkers, and physiological signals Cognitive application -- Neurological visual disorder identifying model -- Receptive field -- Activation map -- Kernel filter -- Conclusion -- References -- Chapter 5: Recurrent neural network model for identifying neurological auditory disorder -- Introduction -- Human auditory system -- Neurological auditory disorder -- Central auditory nervous system -- Cortical deafness -- Recurrent neural network -- Speech recognition -- Auditory event-related potentials -- Sentence boundary disambiguation -- Neurological auditory disorder identifying model -- Audio segmentation -- Phonetic recognition -- Attention mechanism -- Conclusion -- References -- Chapter 6: Recurrent neural network model for identifying epilepsy based neurological auditory disorder -- Introduction -- Related research -- Multiperspective learning techniques -- TSK fuzzy system -- Proposed method -- Shallow feature acquisition of EEG signals -- Shallow feature construction in time-frequency domain -- Acquisition of deep features based on deep learning -- Frequency domain deep feature extraction network -- Time-frequency domain deep feature extraction network -- Multiview TSK blur system based on view weighting -- Experimental study -- Dataset -- Validity analysis -- Numerical analysis of deep feature extraction networks -- Conclusion -- References -- Chapter 7: Dementia diagnosis with EEG using machine learning -- Introduction -- Prevalence of dementia worldwide -- Electroencephalogram -- Cognitive testing and EEG -- Data acquisition -- Preprocessing of EEG signal -- Feature extraction -- Linear approach -- Nonlinear approach -- Classification of dementia -- Discussion -- Conclusion -- References -- Chapter 8: Computational methods for translational brain-behavior analysis -- Introduction -- Computational physiology -- Medical and data scientists -- Translational brain behavioral pattern Intro -- Artificial Intelligence for Neurological Disorders -- Copyright -- Dedication -- Contents -- Contributors -- About the editors -- Preface -- Overview -- Objective -- Organization -- Acknowledgment -- Chapter 1: Early detection of neurological diseases using machine learning and deep learning techniques: A review -- Introduction -- Support vector machine -- Random forest -- Logistic regression -- Convolutional neural network -- Literature review -- Machine learning algorithms -- Deep learning algorithms -- Methodology and result analysis -- Proposed method -- Conclusion -- References -- Chapter 2: A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset -- Introduction -- Literature review -- Materials and methods -- IoT-based Muse headband -- Feature selection -- Datasets -- Feature selection algorithms -- Symmetric uncertainty -- Deep learning model -- LSTM classification -- Result analysis -- Conclusion and discussion -- References -- Chapter 3: Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proli -- Introduction -- How does AD affect the patient's life and normal functioning? -- Can AD onset be avoided or at least be delayed? -- Symptoms -- Pathophysiology of AD -- Management of AD -- Introduction to machine learning and deep learning and their suitability to AD diagnosis -- State of the art/national and international status -- Conclusion -- References -- Further reading -- Chapter 4: Convolutional neural network model for identifying neurological visual disorder -- Introduction -- Human visual system -- Visual cortex -- Vision disorders -- Cortical blindness -- Acquired cortical blindness -- Congenital cortical blindness -- Transient cortical blindness -- Convolutional neural network -- Image recognition -- Image classification Artificial intelligence and disease diagnosis and prediction |
| Title | Artificial Intelligence for Neurological Disorders |
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