Handbook of AI and Data Sciences for Sleep Disorders (eBook, PDF)

Redaktion: Berry, Richard B.; Xian, Xiaochen; Pardalos, Panos M.

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 304
• Erscheinungstermin: 18. Oktober 2024
• Englisch
• ISBN-13: 9783031682636
• Artikelnr.: 71946675

Autorenporträt

¿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 currently an assistant professor in H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. Prior to joining Georgia Tech, she was an assistant professor in the Department of Industrial and Systems Engineering at the University of Florida. She received her B.S. degree in Mathematics and Applied Mathematics from Zhejiang University, China in 2014, and the M.S. degree in Statistics, and the Ph.D. degree in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2017 and 2019. Dr. Xian's research focuses on computationally aware systems with a special interest in novel methodologies in data-driven decision-making and machine learning under constraints to enable theoretically sound and viable analytical tools. Her research has been supported by federal and local agencies including NSF, NIH, the Florida Center for Cybersecurity, and the Florida Space Grant Consortium. She is the recipient of multiple awards, including NIH NIBIB Trailblazer Award, Cottmeyer Family Faculty Fellowships, finalist of INFORMS QSR Best Referred Paper, INFORMS DMDA Workshop Best Paper, and IISE QCRE Best Track Paper, second runner-up of Best Paper Award in IEEE TASE, feature articles in IISE magazine, AIE, and YoungStats. Dr. Xian is an associate editor of IEEE Transactions on Automation Science and Engineering and IEEE International Conference on Automation Science and Engineering.  

Inhaltsangabe

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.