Handbook of AI and Data Sciences for Sleep Disorders

Edited by Richard B. Berry (), Panos M. Pardalos () and Xiaochen Xian ()

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2024
ISBN: 978-3-031-68263-6
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Chapters in this book:

Empowering Sleep Health: Unleashing the Potential of Artificial Intelligence and Data Science in Sleep Disorders

Xin Zan, Feng Liu, Xiaochen Xian and Panos M. Pardalos

Polysomnography Raw Data Extraction, Exploration, and Preprocessing

Malak A. Almarshad, Saiful Islam, Sultan Bahammam, Saad Al-Ahmadi and Ahmed S. BaHammam

Sleep Stage Probabilities Derived from Neurological or Cardiorespiratory Signals by Means of Artificial Intelligence

Peter Anderer, Marco Ross, Andreas Cerny and Pedro Fonseca

From Screening at Clinic to Diagnosis at Home: How AI/ML/DL Algorithms Are Transforming Sleep Apnea Detection

Pei-Lin Lee, Wenbo Gu, Wen-Chi Huang and Ambrose A. Chiang

Modeling and Analysis of Mechanical Work of Breathing

Victoria Ribeiro Rodrigues, Szilard L. Beres, Paul W. Davenport and Nicholas J. Napoli

A Probabilistic Perspective: Bayesian Neural Network for Sleep Apnea Detection

Minhee Kim, Xin Zan and Xiaochen Xian

Automatic and Machine Learning Methods for Detection and Characterization of REM Sleep Behavior Disorder

Matteo Cesari and Irene Rechichi

Sleep Cyclic Alternating Pattern (CAP) as a Neurophysiological Marker of Brain Health

S. A. Bottari, R. Ferri, M. S. Jaffee and John B. Williamson

Deep Learning with Electrocardiograms

Wesley Chorney, Haifeng Wang and Lir-Wan Fan

Machine Learning Automated Analysis Applied to Mandibular Jaw Movements During Sleep: A Window on Polysomnography

Jean-Benoit Martinot, Nhat-Nam Le-Dong and Jean-Louis Pépin

Nightmare Disorder: An Overview

Brian Robertson, Alexander Semenov, Tyler Skluzacek, Han Coburn and Matthew Miller

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DOI: 10.1007/978-3-031-68263-6

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