東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Handbook of AI and data sciences for sleep disorders

Record Type:	Electronic resources : Monograph/item
Title/Author:	Handbook of AI and data sciences for sleep disorders/ edited by Richard B. Berry, Panos M. Pardalos, Xiaochen Xian.
other author:	Berry, Richard B.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	x, 304 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Sleep disorders - Handbooks, manuals, etc. -
Online resource:	https://doi.org/10.1007/978-3-031-68263-6
ISBN:	9783031682636

Handbook of AI and data sciences for sleep disorders
Handbook of AI and data sciences for sleep disorders[electronic resource] /edited by Richard B. Berry, Panos M. Pardalos, Xiaochen Xian. - Cham :Springer Nature Switzerland :2024. - x, 304 p. :ill. (some col.), digital ;24 cm. - Springer optimization and its applications,v. 2161931-6836 ;. - Springer optimization and its applications ;v. 216..

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.

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.

ISBN: 9783031682636

Standard No.: 10.1007/978-3-031-68263-6doiSubjects--Topical Terms:

929034
Sleep disorders
--Handbooks, manuals, etc.

LC Class. No.: RC547

Dewey Class. No.: 616.8498

Handbook of AI and data sciences for sleep disorders
LDR:03296nmm a2200337 a 4500 001 2380361
003 DE-He213
005 20241019125744.0
006 m d
007 cr nn 008maaau
008 250418s2024 sz s 0 eng d
020 $a 9783031682636 $q (electronic bk.)
020 $a 9783031682629 $q (paper)
024 7 $a 10.1007/978-3-031-68263-6 $2 doi
035 $a 978-3-031-68263-6
040 $a GP $c GP
041 0 $a eng
050 4 $a RC547
072 7 $a PBU $2 bicssc
072 7 $a MAT042000 $2 bisacsh
072 7 $a PBU $2 thema
082 0 4 $a 616.8498 $2 23
090 $a RC547 $b .H236 2024
245 1 0 $a Handbook of AI and data sciences for sleep disorders $h [electronic resource] / $c edited by Richard B. Berry, Panos M. Pardalos, Xiaochen Xian.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a x, 304 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer optimization and its applications, $x 1931-6836 ; $v v. 216
505 0 $a 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.
520 $a 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.
650 0 $a Sleep disorders $v Handbooks, manuals, etc. $3 929034
650 0 $a Artificial intelligence $x Medical applications $v Handbooks, manuals, etc. $3 3614642
650 1 4 $a Calculus of Variations and Optimization. $3 3538813
650 2 4 $a Neurology. $3 588698
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Science. $3 3538937
700 1 $a Berry, Richard B. $3 3733758
700 1 $a Pardalos, P. M. $3 3593341
700 1 $a Xian, Xiaochen. $3 3733759
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer optimization and its applications ; $v v. 216. $3 3733760
856 4 0 $u https://doi.org/10.1007/978-3-031-68263-6
950 $a Mathematics and Statistics (SpringerNature-11649)