从言语中测量呼吸速率
Sidharth Abrol1, Biswajit Das1, Srikanth Nallanthighal1
1Philips India Limited, Bengaluru, India.
Digital biomarkers
|April 16, 2025
概括
这项研究使用深度学习来从语音中预测呼吸速率,实现远程患者监测的高精度. 语音作为一个新的虚拟传感器用于呼吸模式.
科学领域:
- 生理学 生理学 生理学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 语音的产生依赖于复杂的呼吸系统协调.
- 精确的呼吸速率监测对于临床诊断和远程患者护理至关重要.
研究的目的:
- 开发和验证一个深度学习模型,直接从语音信号预测呼吸速率.
- 评估使用语音作为呼吸监测的非侵入性虚拟传感器的可行性.
主要方法:
- 利用在双语语音数据 (N=1,005) 上训练的多变量时间序列变压器模型.
- 采用语音编码器嵌入作为深度学习模型的输入功能.
- 评估模型性能,重点关注精度 (±3 BPM) 和在噪音环境中的强度.
主要成果:
- 最好的模型准确地预测了82%的试验对象的呼吸速率在±3 BPM内.
- 一个噪声感知算法证明了该模型对不同水平的背景噪声的弹性.
- 语音被验证为呼吸速率估计的可靠代理.
结论:
- 语音可以作为一个高效和低成本的虚拟传感器来测量呼吸速率.
- 这种方法为推进远程患者监测和远程医疗解决方案提供了巨大的潜力.
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