Handbook of AI and Data Sciences for Sleep Disorders
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 co...
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| Autres auteurs: | , , |
| Format: | Électronique eBook |
| Langue: | anglais |
| Publié: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Édition: | 1st ed. 2024. |
| Collection: | Springer Optimization and Its Applications,
216 |
| Sujets: | |
| Accès en ligne: | https://doi.org/10.1007/978-3-031-68263-6 |
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Table des matières:
- 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.