基于来自人类持续呼吸信号的1D SNN和每个呼吸模式的范围分类方法的多个呼吸模式的检测
Jin-Woo Hong1, Seong-Hoon Kim2, Gi-Tae Han1
1Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了一种新的1D语神经网络 (SNN) 模型和合并分割算法,用于准确的人类呼吸模式检测和分类. 新方法显著提高了在呼吸数据中识别多个呼吸模式的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 信号处理 信号处理
背景情况:
- 人类呼吸系统信息是健康状况分析的宝贵生物识别来源.
- 对呼吸模式的准确分类对于医疗保健应用至关重要.
- 现有的方法在单个分析窗口中难以处理多个模式,从而降低了识别率.
研究的目的:
- 开发一种先进的模型来检测和分类人类呼吸模式.
- 为了解决处理复杂呼吸数据的现有方法的局限性.
- 为了提高识别和分类多种呼吸模式的准确性.
主要方法:
- 提出了一个基于1D罗神经网络 (SNN) 的人类呼吸模式检测模型.
- 引入了一个合并和分割算法,用于在定义区域内分类多个呼吸模式.
- 在呼吸范围分类中的精度计算中使用过交叉点 (IOU).
主要成果:
- 与深度神经网络 (DNN) 相比,实现了大约19.3%的准确性改进.
- 在模式分类方面,与1D卷积神经网络 (CNN) 相比,显示了12.4%的精度提升.
- 在简单的呼吸模式检测中显示出比DNN高14.5%的精度,比1D CNN高5.3%.
结论:
- 拟议的SNN模型和合并分割算法在人类呼吸模式分析中提供了卓越的性能.
- 这种方法有效地克服了复杂的呼吸数据的传统窗口式方法的局限性.
- 这些发现表明,利用呼吸系统数据进行增强的医疗保健诊断方面取得了重大进展.
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