一种可解释的深度学习方法,通过单通道空气流来检测儿科睡眠呼吸暂停
Veronica Barroso-Garcia1,2, Fernando Vaquerizo-Villar1,2, Gonzalo C Gutierrez-Tobal1,2
1Biomedical Engineering GroupUniversidad de Valladolid Valladolid 47011 Spain.
IEEE journal of translational engineering in health and medicine
|January 8, 2026
概括
这项研究引入了使用空气流数据的深度学习模型,以准确诊断儿科阻塞性睡眠呼吸暂停 (OSA). 可解释的AI方法提高了儿童早期客观诊断的可信性.
科学领域:
- 人工智能在医学中的应用
- 儿科睡眠医学 儿科睡眠医学
- 生物医学信号处理
背景情况:
- 单通道空气流分析显示,对诊断儿科阻塞性睡眠呼吸暂停 (OSA) 有希望.
- 使用特征工程的传统方法限制了复杂的呼吸模式识别和自动诊断性能.
- 深度学习和可解释的AI (XAI) 提供了在OSA诊断中提高准确性和透明度的潜力.
研究的目的:
- 开发和验证一个深度学习模型,以使用单通道空气流来估计小儿OSA的严重程度.
- 通过可解释的AI技术,确保自动化OSA诊断的透明度.
- 为了比较Grad-CAM和SHAP在OSA检测中识别关键空气流特征的有效性.
主要方法:
- 利用了来自四个儿科数据集的3,672个夜间空气流记录.
- 训练了一个卷积神经网络 (CNN) 回归模型来估计呼吸暂停-呼吸暂停指数 (AHI) 并预测OSA严重程度.
- 使用梯度加权类激活映射 (Grad-CAM) 和夏普利添加式扩展 (SHAP) 来实现模型的解释性.
主要成果:
- 在CNN模型中,估计和实际AHI之间取得了很高的一致性 (ICC 0.690.87).
- 在三个OSA重度切线 (准确率82.03%99.03%) 中表现出高的诊断性能.
- 解释性分析证实了CNN对呼吸暂停事件的准确识别,Grad-CAM和SHAP提供了互补的见解.
结论:
- 开发的可解释的深度学习工具通过空气流数据准确地检测儿科OSA.
- 这种方法促进了早期的客观诊断,并支持临床决策.
- 可解释的AI增强了模型的可信性和可用性,为临床翻译铺平了道路.
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