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Handbook of AI and Data Sciences for Sleep Disorders: Springer Optimization and Its Applications, cartea 216

Editat de Richard B. Berry, Panos M. Pardalos, Xiaochen Xian
en Limba Engleză Hardback – 2 noi 2024
The rise of lifestyle changes resulting from constant connectivity, irregular work schedules, heightened stress, and disruptive sleep patterns, have contributed to increasing insomnia rates.  Exacerbated by the COVID-19 pandemic, sleep disorders are more prevalent than ever. This handbook offers a comprehensive exploration of the fusion of Artificial Intelligence (AI) and data science within the realm of sleep disorders, presenting innovative approaches to diagnosis, treatment, and personalized care.
The interdisciplinary nature of this handbook fosters collaboration between experts from diverse fields, including computer science, engineering, neuroscience, medicine, public health, AI, data science, and sleep medicine. Each chapter delves into specific aspects of sleep disorder analysis, innovative methodologies, novel insights, and real-world applications that showcase the transformative potential of AI and data science in sleep medicine, from analyzing sleep patterns and predicting disorder risk factors to utilizing big data analytics for large-scale epidemiological studies. This handbook hopes to offer a comprehensive resource for researchers, clinicians, and policymakers striving to address the challenges in sleep medicine.
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Specificații

ISBN-13: 9783031682629
ISBN-10: 3031682629
Pagini: 350
Ilustrații: Approx. 350 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.62 kg
Ediția:2025
Editura: Springer Nature Switzerland
Colecția Springer
Seria Springer Optimization and Its Applications

Locul publicării:Cham, Switzerland

Cuprins

Empowering Sleep Health: Unleashing the Potential of Artificial Intelligence and Data Science in Sleep Disorders.- Polysomnography Raw Data Extraction, Exploration, and Preprocessing.- Sleep stage probabilities derived from neurological or cardio-respiratory signals by means of artificial intelligence.- From Screening at Clinic to Diagnosis at Home: How AI/ ML/DL Algorithms are Transforming Sleep Apnea Detection.- Modeling and Analysis of Mechanical Work of Breathing.- A Probabilistic Perspective: Bayesian Neural Network for Sleep Apnea Detection.- Automatic and machine learning methods for detection and characterization of REM sleep behavior disorder.- Sleep Cyclic Alternating Pattern (CAP) as a Neurophysiological Marker of Brain Health.- Deep Learning with Electrocardiograms.- Machine learning automated analysis applied to mandibular jaw movements during sleep: a window on polysomnography.- Nightmare disorder: An Overview.

Notă biografică

​Richard B. Berry is Medical Director, UF Health Sleep Disorders Center. His clinical interests include sleep disorders, obstructive and central sleep apnea, restless leg syndrome, narcolepsy, noctural respiratory failure, and insomnia. Research interests include upper airway physiology, pharmacological treatment of sleep apnea, mechanisms of respiratory arousal, and auto positive airway pressure treatment.

Panos M. Pardalos serves as Distinguished Professor of Industrial and Systems Engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor in Industrial & Systems Engineering. He is also an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the Director of the Center for Applied Optimization. Dr. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing. He has co-authored and co-edited more than 30 books, as well as publishing more than 600 journal articles and conference proceedings. Prof. Pardalos is a Fellow of AAAS (American Association for the Advancement of Science), Fellow of American Institute for Medical and Biological Engineering (AIMBE), and EUROPT. He is a Distinguished International Professor by the Chinese Minister of Education; Honorary Professor of Anhui University of Sciences and Technology, China; Elizabeth Wood Dunlevie Honors Term Professor; Honorary Doctor, V.M. Glushkov Institute of Cybernetics of The National Academy of Sciences of Ukraine; Foreign Associate Member of Reial Academia de Doctors, Spain; and Advisory board member of the Centre for Optimisation and Its Applications, Cardiff University, UK. He is also the recipient of UF 2009 International Educator Award; Medal (in recognition of broad contributions in science and engineering) of the University of Catani, Italy; EURO Gold Medal (EGM); Honorary Doctor of Science Degree, Wilfrid Laurier University, Canada; Senior Fulbright Specialist Award; University of Florida Research Foundation Professorship; and IBM Achievement Award.

Xiaochen Xian is an assistant professor in the UF ISE department. Her research interest mainly focuses on big data analytics and system informatics to develop data-driven methodologies for quality improvement in complex systems. Specifically, her research includes big data stream monitoring and sampling, engineering knowledge-enhanced complex process modeling and diagnosis, and system informatics and spatiotemporal real-time prediction. Her research leads to immediate applications in manufacturing, healthcare, environmental monitoring, and traffic, etc. She is also a member of INFORMS, IISE, and SME.



Textul de pe ultima copertă

The rise of lifestyle changes resulting from constant connectivity, irregular work schedules, heightened stress, and disruptive sleep patterns, have contributed to increasing insomnia rates.  Exacerbated by the COVID-19 pandemic, sleep disorders are more prevalent than ever. This handbook offers a comprehensive exploration of the fusion of Artificial Intelligence (AI) and data science within the realm of sleep disorders, presenting innovative approaches to diagnosis, treatment, and personalized care.
The interdisciplinary nature of this handbook fosters collaboration between experts from diverse fields, including computer science, engineering, neuroscience, medicine, public health, AI, data science, and sleep medicine. Each chapter delves into specific aspects of sleep disorder analysis, innovative methodologies, novel insights, and real-world applications that showcase the transformative potential of AI and data science in sleep medicine, from analyzing sleep patterns and predicting disorder risk factors to utilizing big data analytics for large-scale epidemiological studies. This handbook hopes to offer a comprehensive resource for researchers, clinicians, and policymakers striving to address the challenges in sleep medicine.

Caracteristici

First book to summarize AI and data science techniques in the application of sleep disorders Contains fundamental knowledge and state-of-the-art methodologies Interdisciplinary content appeals to an expansive readership from engineering, medicine, mathematics, computer science, and business