Osa-Sanchez Ainhoa, Ramos-Martinez-de-Soria Javier, Mendez-Zorrilla Amaia, Ruiz Ibon Oleagordia, Garcia-Zapirain Begonya
eVIDA Research Group, University of Deusto, Bilbao, 48007, Spain.
J Med Syst. 2025 May 19;49(1):66. 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.
睡眠呼吸暂停是一种在全球影响数百万人的普遍疾病，近年来因其对公众健康和生活质量的重大影响而受到越来越多的关注。可穿戴设备与人工智能技术的融合彻底改变了睡眠呼吸暂停的治疗和诊断方式。利用可穿戴设备的便携性和传感器，结合人工智能算法，能够对睡眠模式进行实时监测和准确分析，为患有睡眠呼吸暂停的人提供早期检测和个性化干预。本文对用于睡眠呼吸暂停诊断的最新人工智能技术、可穿戴设备、数据类型和预处理方法的当前技术水平进行了系统综述。使用了四个数据库，筛选前的结果报告了2020年至2024年发表的249项研究。筛选后，有28项研究符合纳入标准。这篇综述揭示了近年来的一种趋势，即涉及贴片、时钟和手环的方法越来越多地与卷积神经网络相结合，产生了有前景的结果，特别是与迁移学习技术相结合时。我们观察到，各种算法及其组合的结果也依赖于用于训练的数据的数量和类型。研究结果表明，采用不同神经网络与卷积层的多种组合有助于开发出更精确的睡眠呼吸暂停早期诊断系统。
