Computational Intelligence Techniques In Diagnosis Of Brain Diseases
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Computational Intelligence Techniques in Diagnosis of Brain Diseases
This book highlights a new biomedical signal processing method of extracting a specific underlying signal from possibly noisy multi-channel recordings, and shows that the method is suitable for extracting independent components from the measured electroencephalogram (EEG) signal. The system efficiently extracts memory spindles and is also effective in Alzheimer seizures. Current developments in computer hardware and signal processing have made it possible for EEG signals or “brain waves” to communicate between humans and computers – an area that can be extended for use in this domain.
Handbook of Computational Intelligence in Biomedical Engineering and Healthcare
Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques. Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/unstructured data on experimental analysis. Presents a comprehensive handbook that covers an Introduction to Computational Intelligence in Biomedical Engineering and Healthcare, Computational Intelligence Techniques, and Advanced and Emerging Techniques in Computational Intelligence Helps readers analyze and do advanced research in specialty healthcare applications Includes links to websites, videos, articles and other online content to expand and support primary learning objectives
Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders
This book delves into the transformative potential of artificial intelligence (AI) and machine learning (ML) as game-changers in diagnosing and managing neurodisorder conditions. It covers a wide array of methodologies, algorithms, and applications in depth. Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders equips readers with a comprehensive understanding of how computational intelligence empowers healthcare professionals in the fight against neurodisorders. Through practical examples and clear explanations, it explores the diverse applications of these technologies, showcasing their ability to analyze complex medical data, identify subtle patterns, and contribute to the development of more accurate and efficient diagnostic tools. The authors delve into the exciting possibilities of AI-powered algorithms, exploring their ability to analyze various data sources like neuroimaging scans, genetic information, and cognitive assessments. They also examine the realm of ML for pattern recognition, enabling the identification of early disease markers and facilitating timely intervention. Finally, the authors also address the critical challenges of data privacy and security, emphasizing the need for robust ethical frameworks to safeguard sensitive patient information. This book aims to spark a conversation and foster collaboration among researchers, clinicians, and technologists, and will assist radiologists and neurologists in making precise diagnoses with enhanced accuracy.