使用耳内麦克风对呼吸阶段和路径进行分类
Malahat H K Mehrban1, Jérémie Voix1,2, Rachel E Bouserhal1,2
1École de technologie supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
智能入耳设备 (可听设备) 可以使用机器学习可靠地监测呼吸模式. 这种方法克服了以前方法的局限性,使得持续的健康跟踪成为可能.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习应用 机器学习应用
- 可穿戴的健康技术
背景情况:
- 智能入耳设备 (可听设备) 越来越多地用于健康监测.
- 现有的耳内呼吸监测依赖于峰值检测,它对运动工件敏感.
- 需要使用可听设备进行持续呼吸监测的强大方法.
研究的目的:
- 用入耳麦克风来分类呼吸路径和呼吸阶段.
- 评估机器学习的有效性,特别是XGBoost,用于此分类任务.
- 为了证明可用于持续呼吸监测的可听设备的可行性.
主要方法:
- 利用现有的呼吸声数据库,通过耳内麦克风捕获呼吸声.
- 由于数据集的大小小小,使用了机器学习分类器XGBoost.
- 为二进制路径,二进制相位和四类路径和相位识别进行培训和评估的分类器.
主要成果:
- 在二进制路径分类中获得了86.8%的准确性.
- 在二进制相位分类中获得了74.1%的准确性.
- 在四类路径和阶段分类方面达到67.2%的准确性,在回忆和F1得分方面表现优于现有的算法.
结论:
- 这项研究证明了使用可听设备用于持续呼吸监测的可行性.
- XGBoost提供了一种可靠的机器学习方法,用于从耳内录音中分类呼吸模式.
- 这项技术有可能增强远程健康监测能力.
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