Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review.
Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality of life. The integration of wearable devices and artificial intelligence technologies has revolutionized the treatme...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 26 |
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| Autores principales: | , , , , |
| Formato: | pictorial research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
5/19/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280411&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280411 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 5/19/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280411 185280411 185280411 10.1007/s10916-025-02199-8 185280411 ppf: 1 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review. aug: au: Osa-Sanchez, Ainhoa Ramos-Martinez-de-Soria, Javier Mendez-Zorrilla, Amaia Ruiz, Ibon Oleagordia Garcia-Zapirain, Begonya affil: https://ror.org/00ne6sr39 eVIDA Research Group, University of Deusto, 48007, Bilbao, Spain sug: subj: Sleep Apnea Syndromes Diagnosis Wearable Sensors Artificial Intelligence Human Systematic Review Funding Source Convolutional Neural Networks Algorithms Machine Learning Deep Learning PubMed Plethysmography Electrocardiography Oxygen Saturation Electromyography Electroencephalography ab: Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality of life. The integration of wearable devices and artificial intelligence technologies has revolutionized the treatment and diagnosis of sleep apnea. Leveraging the portability and sensors of wearable devices, coupled with AI algorithms, has enabled real-time monitoring and accurate analysis of sleep patterns, facilitating early detection and personalized interventions for people suffering from sleep apnea. This article presents a systematic review of the current state of the art in identifying the latest artificial intelligence techniques, wearable devices, data types, and preprocessing methods employed in the diagnosis of sleep apnea. Four databases were used and the results before screening report 249 studies published between 2020 and 2024. After screening, 28 studies met the inclusion criteria. This review reveals a trend in recent years where methodologies involving patches, clocks and rings have been increasingly integrated with convolutional neural networks, producing promising results, particularly when combined with transfer learning techniques. We observed that the outcomes of various algorithms and their combinations also rely on the quantity and type of data utilized for training. The findings suggest that employing multiple combinations of different neural networks with convolutional layers contributes to the development of a more precise system for early diagnosis of sleep apnea. pubtype: Academic Journal doctype: pictorial research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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