一个基于受约束交叉网络的新型特征提取器,用于检测睡眠状态
1College of Science, Nanjing Forestry University, Nanjing, 210037, People's Republic of China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一种改进的功能提取器,使用受约束交叉网络来准确地检测手腕设备的睡眠和清醒状态. 新方法显著提高了分类准确性和F1分数,超过了传统方法.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 越来越多的人对健康的生活方式越来越感兴趣,因此需要精确的睡眠监测.
- 现有的手腕穿戴睡眠检测方法缺乏效率和准确性.
- 传统的机器学习和启发式算法在复杂的睡眠和清醒分类方面遇到了困难.
研究的目的:
- 为了提高睡眠-觉醒二进制分类的准确性,使用手腕穿戴设备的数据.
- 基于受约束交叉网络开发一个改进的特征提取器.
- 为了解决当前睡眠检测技术的局限性.
主要方法:
- 开发了一种包含特征导出和交互模块的新型特征提取器.
- 特性导出模块使用扩展卷积,封闭的循环单位和注意力机制.
- 功能交互模块使用基于DCN-v2的受约束交叉网络,用于高阶功能交互.
主要成果:
- 获得了91.14%的F1得分和95.70%的准确性,超过了基于CNN的方法.
- 采用无磨损识别面具进一步提高了性能,达到94.25%的F1分数和97.38%的准确性.
- 约束交叉网络对特征提取器的性能做出了重大贡献.
结论:
- 拟议的基于受约束交叉网络的特征提取器显著提高了睡眠和清醒分类的准确性.
- 这种方法为睡眠分析提供了比传统深度神经网络更有效,更准确的替代方案.
- 显式特征构造具有未来在睡眠研究中手动特征开发的潜力.
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