Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.

Journal: Sleep & breathing = Schlaf & Atmung

PMID: 39190088

Abstract

PURPOSE: This study aims to develop sleep apnea screening models with overnight SpO2 data, and to investigate the impact of the SpO2 data granularity on model performance.

Authors

Zilu Liang

Ubiquitous and Personal Computing Lab, Kyoto University of Advanced Science (KUAS), 18 Yamanouchi Gotanda-cho, Ukyo-ku, Kyoto, Japan. liang.zilu@kuas.ac.jp.

Keywords

Adult Aged Female Humans Machine Learning Male Mass Screening Middle Aged Models, Statistical Oximetry Oxygen Saturation Polysomnography Sleep Apnea Syndromes Sleep Apnea, Obstructive

External Resources

View on PubMed Access via DOI PubMed (39190088)

Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.

Abstract

Authors

Keywords

External Resources

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