ESSN:一个高效的睡眠序列网络,用于自动睡眠分期
IEEE journal of biomedical and health informatics
|August 14, 2024
概括
本研究介绍了一种高效睡眠序列网络 (ESSN),用于在计算能力有限的设备上准确的自动睡眠分阶段. ESSN提高了效率,减少了N1阶段的混,为现有方法提供了有竞争力的替代方案.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 睡眠医学 睡眠医学
背景情况:
- 先进的自动睡眠分阶段算法显示出希望,但在有限的计算资源和N1阶段混乱的情况下扎.
- 现有的方法不适合用于便携式睡眠检测或消费者级睡眠障碍查.
研究的目的:
- 开发一个高效的自动睡眠分阶段算法 (ESSN),适合低功耗计算环境.
- 解决目前睡眠分期算法中普遍存在的N1阶段混问题.
- 为了实现高精度的自动睡眠分期与降低计算成本.
主要方法:
- 提出了一个高效的睡眠序列网络 (ESSN),具有专门的计算效率结构.
- 引入了一个新的N1结构损失函数,利用N1过渡概率来缓解混乱.
- 在SHHS数据集上对ESSN进行了评估,其中包括5,793名受试者.
主要成果:
- 在SHHS数据集上,ESSN的整体准确度为88.0%,宏F1为81.2%,Cohen的kappa为0.831.
- 该模型以0.27M参数和0.35G浮点运算为200个样本输入的低计算要求.
- 在相同的硬件上,ESSN的推断速度是L-SeqSleepNet的两倍,准确度也更高.
结论:
- ESSN为自动睡眠分阶段提供了高效和准确的解决方案,特别是在资源有限的环境中.
- 新的N1结构损失有效地减少了N1阶段的混乱,提高了诊断可靠性.
- 对于临床辅助和消费者应用的最先进方法,ESSN具有竞争优势.
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