Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review

• Published in: Journal of medical systems Vol. 49; no. 1; p. 66
• Main Authors: Osa-Sanchez, Ainhoa, Ramos-Martinez-de-Soria, Javier, Mendez-Zorrilla, Amaia, Ruiz, Ibon Oleagordia, Garcia-Zapirain, Begonya
• Format: Journal Article
• Language: English
• Published: New York Springer US 19.05.2025; Springer Nature B.V
• Subjects: Algorithms; Apnea; Artificial Intelligence; Artificial neural networks; Clocks; Data analysis; Diagnosis; Health Informatics; Health Sciences; Humans; Machine learning; Medical diagnosis; Medical equipment; Medicine; Medicine & Public Health; Neural networks; Neural Networks, Computer; Observational learning; Polysomnography; Precision medicine; Public health; Quality of life; Real time; Screening; Sensors; Sleep apnea; Sleep Apnea Syndromes - diagnosis; Sleep disorders; Statistics for Life Sciences; Systematic review; Transfer learning; Wearable computers; Wearable Electronic Devices; Wearable technology; Wearable devices; Artificial intelligence; Biomedical data; Sleep apnea disease
• ISSN: 1573-689X; 0148-5598; 1573-689X
• DOI: 10.1007/s10916-025-02199-8

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.