呼吸模式分类的双流卷积神经网络:呼吸系统疾病患者的实时监测
Jinho Park1, Thien Nguyen1, Soongho Park1,2
1Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 49 Convent Dr., Bethesda, MD 20894, USA.
Bioengineering (Basel, Switzerland)
|July 27, 2024
概括
一个新的双流卷积神经网络 (TCNN) 准确地使用可穿戴传感器数据对呼吸模式进行分类. 这种人工智能模型为呼吸系统疾病患者提供了强大的,高效的持续监测.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 持续监测呼吸模式对于管理传染性呼吸道疾病至关重要.
- 现有的方法可能缺乏准确性或实时患者监控的效率.
- 可穿戴近红外光谱 (NIRS) 提供了一种非侵入性的方法来捕获生理数据.
研究的目的:
- 开发和评估一个双流卷积神经网络 (TCNN),用于准确的呼吸模式分类.
- 将TCNN的表现与单流CNN (SCNN) 和其他最先进的模型进行比较.
- 评估TCNN在分类正常,缓慢,快速和喘息模式方面的有效性.
主要方法:
- 设计了一个TCNN架构,集成了一个基于CNN的自动编码器用于特征提取和分类器.
- TCNN处理来自胸部组织的血液动力学反应数据,通过可穿戴NIRS测量.
- 通过对14名健康成年人进行四种呼吸模式的分类来评估性能,并与SCNN和随机森林模型进行比较.
主要成果:
- TCNN获得了最高的分类准确率94.63%,超过了随机森林 (88.49%) 和SCNN (92.03%).
- 与SCNN相比,TCNN的准确性提高了2.6%.
- 与SCNN不同的是,TCNN有效地解决了与网络深度增加相关的学习性能下降的问题.
结论:
- TCNN是用于呼吸模式分类的强大而准确的模型.
- 该TCNN提供了一个有效的解决方案,用于持续的患者监测,需要更少的参数和计算.
- 这种由人工智能驱动的方法显示了改善呼吸系统疾病管理的前景.
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