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通过深度学习的增强呼吸信号建模,对新生儿进行自动低氧和呼吸暂停的识别
Abel Jaba Deva Krupa1, Bhoovi Chauhan1, Shaik Khaja Saif Azam1
1Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India.
Scientific reports
|November 19, 2025
概括
这项研究引入了新生儿呼吸监测的合成数据框架,使机器学习 (ML) 模型能够准确评估婴儿的呼吸模式并检测呼吸困难.
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
- 新生儿医学 新生儿医学
背景情况:
- 新生儿呼吸系统监测对于婴儿健康至关重要.
- 有限的实时临床数据阻碍了用于呼吸评估的先进机器学习 (ML) 模型的开发.
- 现有的方法难以捕捉正常和病态婴儿呼吸模式的细微差别.
研究的目的:
- 为新生儿呼吸循环开发一种合成信号生成框架,具有生理学准确性.
- 创建一个强大的基于ML的系统来对新生儿呼吸系统状态进行分类.
- 评估不同ML模型在检测新生儿呼吸困难方面的有效性.
主要方法:
- 合成新生儿呼吸信号的生成以生物现实主义模拟正常和病态呼吸 (呼吸暂停,缺氧,周期性呼吸).
- 实现一个特征提取管道,分析时间和频率域特征.
- 使用卷积神经网络 (CNN),CNN-BiLSTM和随机森林 (RF) 的呼吸系统状态的分类.
主要成果:
- 该CNN-BiLSTM模型实现了最高的分类准确率96.16%.
- 开发的模型成功地分类了各种呼吸状态,优于独立的CNN和RF模型.
- 合成新生儿数据证明在基于ML的呼吸困难评估中有效.
结论:
- 合成新生儿呼吸系统数据可以有效地训练ML模型进行呼吸应急评估.
- 在CNN-BiLSTM架构显示了实时新生儿呼吸监测的显著前景.
- 未来的工作包括使用电子织品实现硬件,并使用真实新生儿数据集进行临床验证.
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