DABiG:使用混合深度学习与最佳特征选择的混合深度学习进行呼吸模式分类
P Kaleeswari1, R Ramalakshmi2, T Arun Prasath3
1Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India.
概括
这项研究使用传感器数据和一种新型深度学习模型,DABIG,来对呼吸模式进行分类. 适应性黑猩猩优化 (AdCO) 算法增强了特征选择,以提高呼吸分析的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 机器学习用于健康
背景情况:
- 呼吸模式反映了情绪和身体健康.
- 对呼吸模式的分析提供了对健康状况的见解.
研究的目的:
- 使用传感器数据对各种呼吸模式进行分类.
- 开发一个全面的方法来识别呼吸模式.
主要方法:
- 利用来自两个传感器的陀螺仪和加速度计数据用于六种呼吸模式.
- 应用Min-Max规范化用于数据预处理.
- 引入了适应性黑猩猩优化 (AdCO) 用于特征选择.
- 开发了一种混合深度学习模型,DABIG,采用双向门式循环单元 (BiGRU).
主要成果:
- 阿德科展示了增强的融合和全球最佳特征选择.
- DABiG模型包含了空间和时间注意力机制.
- 注意力机制改善了对相关数据区域和时间步骤的关注.
结论:
- 空间和时间注意力机制显著增强特征提取.
- 拟议的方法提高了呼吸模式分类的准确性.
- 这种方法提供了一个强大的工具,通过呼吸来分析生理和情绪状态.
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