使用多种数据和一维卷积神经网络识别阻塞性睡眠呼吸暂停中的并发症
Kristina Zovko1, Ljiljana Šerić1, Toni Perković1
1Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, 21000 Split, Croatia.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
深度学习模型可以使用SpO2和空气流数据预测阻塞性睡眠呼吸暂停并发症. 这种方法提供了有效的查,而不是完全的多睡眠学,帮助临床决策支持.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 睡眠医学研究 睡眠医学研究
背景情况:
- 深度学习 (DL) 的进步使得能够整合各种生物医学数据来预测疾病.
- 阻塞性睡眠呼吸暂停 (OSA) 经常与主要的并发症有关.
- 准确的并发症鉴定对于有效的OSA患者管理至关重要.
研究的目的:
- 开发和评估一个多模式的DL框架,用于OSA并发症的多标签分类 (MLC).
- 为了利用生理时间序列信号 (SpO2,空气流) 和临床数据进行并发症预测.
- 评估框架的稳定性和跨患者人口统计数据的概括性.
主要方法:
- 一项对144名OSA患者进行的回顾性观察研究.
- 开发一个1D卷积神经网络 (1D-CNN) 来融合SpO2,空气流和临床数据.
- 使用Optuna进行模型训练,用于超参数优化和对类不平衡的加权损失.
主要成果:
- 与传统的ML分类器相比,1D-CNN模型实现了更高的性能 (宏观AUC-ROC=0.731,AUC-PR=0.750).
- 该模型在不同年龄,性别和BMI组中显示出一致的表现,这表明人口统计偏差最小.
- 研究人员发现,生理信号 (SpO2,空气流) 编码了相关疾病特异性模式.
结论:
- SpO2和空气流信号可以实现有效和可扩展的OSA相关并发症查.
- 拟议的DL框架为睡眠医学中的临床决策支持系统提供了方法论基础.
- 未来的工作应该集中在多中心数据集上,以提高模型的解释性和临床采用.
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