La Porta Nicolo, Scafa Stefano, Papandrea Michela, Molinari Filippo, Puiatti Alessandro
Faculty of InformaticsUniversità della Svizzera Italiana (USI) 6900 Lugano Switzerland.
Institute of Information Systems and Networking (ISIN)University of Applies Sciences and Arts of Southern Switzerland (SUPSI) 6962 Lugano-Viganello Switzerland.
IEEE Open J Eng Med Biol. 2024 Nov 28;6:202-211. doi: 10.1109/OJEMB.2024.3508477. eCollection 2025.
The gold standard for detecting the presence of apneic events is a time and effort-consuming manual evaluation of type I polysomnographic recordings by experts, often not error-free. Such acquisition protocol requires dedicated facilities resulting in high costs and long waiting lists. The usage of artificial intelligence models assists the clinician's evaluation overcoming the aforementioned limitations and increasing healthcare quality. The present work proposes a machine learning-based approach for automatically recognizing apneic events in subjects affected by sleep apnea-hypopnea syndrome. It embraces a vast and diverse pool of subjects, the Wisconsin Sleep Cohort (WSC) database. An overall accuracy of 87.2[Formula: see text]1.8% is reached for the event detection task, significantly higher than other works in literature performed over the same dataset. The distinction between different types of apnea was also studied, obtaining an overall accuracy of 62.9[Formula: see text]4.1%. The proposed approach for sleep apnea events recognition, validated over a wide pool of subjects, enlarges the landscape of possibilities for sleep apnea events recognition, identifying a subset of signals that improves State-of-the-art performance and guarantees simple interpretation.
检测呼吸暂停事件存在的金标准是由专家对I型多导睡眠图记录进行耗时费力的人工评估，且往往并非完全无误。这样的采集方案需要专门的设备，导致成本高昂且等待名单很长。人工智能模型的使用有助于临床医生的评估，克服了上述局限性并提高了医疗质量。本研究提出了一种基于机器学习的方法，用于自动识别受睡眠呼吸暂停低通气综合征影响的受试者中的呼吸暂停事件。它采用了大量不同的受试者群体，即威斯康星睡眠队列（WSC）数据库。对于事件检测任务，总体准确率达到了87.2±1.8%，显著高于在同一数据集上进行的其他文献研究。还研究了不同类型呼吸暂停之间的区分，总体准确率为62.9±4.1%。所提出的睡眠呼吸暂停事件识别方法在大量受试者中得到了验证，拓宽了睡眠呼吸暂停事件识别的可能性范围，识别出了一组能提高现有技术水平性能并保证易于解释的信号子集。
