DynamicSleepNet:一个多出口的神经网络,具有适应性推断时间,用于睡眠阶段分类
Wang Wenjian1, Xiao Qian1, Xue Jun1
1School of Information Science, Yunnan University, Kunming, China.
Frontiers in physiology
|May 30, 2023
概括
本研究介绍了DynamicSleepNet,这是一个高效的深度学习模型,用于使用电生理信号自动分类睡眠阶段. 它实现了高精度,同时显著降低了计算成本和模型大小,使其适合实际应用.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 睡眠阶段的分类对于诊断睡眠障碍至关重要.
- 专家的手动分类是耗时和劳动密集的.
- 使用多模电生理学信号的深度学习模型已经提高了准确性,但受到计算冗余和大模型尺寸的影响.
研究的目的:
- 开发一种高效准确的深度学习模型,用于自动分类睡眠阶段.
- 解决现有多式联网网络中的计算冗余和大型模型大小问题.
- 创建一个动态平衡准确性和效率的模型.
主要方法:
- 提出了DynamicSleepNet,这是一个新的网络架构,包含有效特征提取模块 (EFEM) 和分类器模块.
- 在抑制噪音的同时,EFEM可以提取和增强相关的信号特征.
- 实施了早期退出策略,在该策略中,分类不确定性较低的样本在不通过整个网络进行处理的情况下输出.
主要成果:
- 与基线模型相比,DynamicSleepNet 在四个数据集中实现了更高的准确性.
- 该模型显示了可比或较低的参数数量显著提高速度 (几到几十倍更快).
- 精度和效率的动态调整得到了验证.
结论:
- DynamicSleepNet提供了一种有效的解决方案,用于高效和准确的自动睡眠阶段分类.
- 该模型的自适应推断时间和降低的复杂性使其适合在资源有限的设备上部署.
- 这种方法促进了深度学习在睡眠医学中的实际应用.
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