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,...
Uložené v:
| Hlavní autori: | , , , |
|---|---|
| Médium: | E-kniha |
| Jazyk: | English |
| Vydavateľské údaje: |
Chantilly
Elsevier Science & Technology
2022
Academic Press |
| Vydanie: | 1 |
| Predmet: | |
| ISBN: | 0323902774, 9780323902779 |
| On-line prístup: | Získať plný text |
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- 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