深度学习方法通过SpO2信号来评估儿科睡眠呼吸暂停的严重程度
Erfan Mortazavi1, Bahram Tarvirdizadeh2, Khalil Alipour1
1Advanced Service Robots (ASR) Laboratory, Department of Mechatronics Engineering, School of Intelligent Systems Engineering, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.
这项研究引入了一种非侵入性深度学习方法,使用血氧和 (SpO2) 信号来评估儿科睡眠呼吸暂停-低呼吸暂停 (SAH) 的严重程度. 在CNN-BiGRU-Attention模型中,对儿童的呼吸暂停-呼吸暂停指数 (AHI) 的估计显示出更高的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 儿科睡眠医学 儿科睡眠医学
背景情况:
- 儿童睡眠呼吸暂停-低呼吸暂停 (SAH) 诊断传统上依赖于多睡眠学 (PSG),这对儿童来说可能是痛苦的.
- 对于儿科SAH,需要使用更少侵入性和更适合儿童的诊断方法.
- 血氧和 (SpO2) 信号为非侵入性监测提供了一个潜在的替代方案.
研究的目的:
- 开发和评估深度学习模型,用于估计儿科SAH中的SpO2信号的呼吸暂停-呼吸暂停指数 (AHI).
- 为了比较基于ResNet和CNN-BiGRU-Attention模型在分类SAH严重性的表现.
- 评估这些模型的诊断能力与已建立的AHI值相比.
主要方法:
- 两种深度学习模型,基于ResNet和注意力增强的CNN-BiGRU,被用来分析1D SpO2信号.
- 该CHAT数据集包括844个儿科SpO2信号,分为培训 (60%),测试 (30%) 和验证 (10%) 集.
- 对于可靠的模型评估的验证子集,应用了三重交叉验证方法.
主要成果:
- 该CNN-BiGRU-Attention模型实现了75.95%的优异平均准确率和0.63.3的卡帕得分.
- 在四个SAH严重程度类别中,ResNet模型获得了72.9%的平均准确率和0.57的kappa得分.
- 这两种模型都在与常见的AHI值 (每小时1,5,10事件) 进行基准比较时,在确定儿科SAH方面表现出有效性.
结论:
- 对SpO2信号的深度学习分析为儿科SAH诊断提供了一个有希望的非侵入性方法.
- 增加注意力的CNN-BiGRU模型在估计AHI和分类SAH严重程度方面表现出更高的有效性.
- 这项研究代表了改善儿科SAH诊断过程的重要一步,提供了更适合儿童的替代方案.
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