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相关实验视频

Updated: Jun 15, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

474

MLS-Net:一种自动睡眠阶段分类器,利用小鼠的多模式生理信号.

Chengyong Jiang1, Wenbin Xie1, Jiadong Zheng1

  • 1State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China.

Biosensors
|August 28, 2024
PubMed
概括

我们开发了MLS-Net,这是一种用于小鼠自动睡眠阶段分类的新型神经网络模型. 该模型集成了特征提取和深度学习,以实现高精度的睡眠阶段分类使用多式联络信号.

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科学领域:

  • 神经科学是一个神经科学.
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 自动睡眠阶段分类 (ASSC) 传统上使用基于特征的方法或深度神经网络 (DNN).
  • 基于特征的方法提供可解释性,但缺乏时空上下文,而DNN捕获复杂的模式,但遭受过度匹配和高计算成本.
  • 现有的方法在动物睡眠研究中的最终用户应用中,在准确性和效率方面面临挑战.

研究的目的:

  • 开发一种新的神经网络模型,MLS-Net,用于准确高效的小鼠自动睡眠分期.
  • 整合特征提取和深度学习的优势,克服现有的ASSC方法的局限性.
  • 利用包括EEG,EMG和眼睛运动在内的多式睡眠信号,以提高分类性能.

主要方法:

  • 开发了MLS-Net,一种神经网络模型,结合了来自多式联络信号 (EEG,EMG,EMs) 的时间和光谱特征.
  • 整合了双向长期短期记忆 (bi-LSTM) 网络,以捕捉睡眠数据中的时空非线性动态.
  • 通过使用多式联网数据集,对传统基于特征的和其他神经网络算法进行MLS-Net性能评估.

主要成果:

  • 对于老鼠的睡眠阶段,MLS-Net实现了90.4%的整体分类准确度.
  • 实现了快速眼动 (REM) 睡眠分类的高性能:91.1%的精度,84.7%的灵敏度,87.5%的F1-Score.
关键词:
关闭的ASSC公司.在MLS-Net中,使用的是MLS-Net.眼睛的运动 眼睛的运动多模式信号多模式信号

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  • 在多式模式睡眠数据集上表现优于现有的神经网络和基于特征的算法.

    • 结论:

    • MLS-Net有效地整合了功能工程和深度学习,用于在小鼠中实现卓越的自动化睡眠分期.
    • 该模型展示了强大的性能和在睡眠研究中更广泛应用的潜力.
    • MLS-Net提供了一个有前途的解决方案,用于准确和计算高效地分析多式联网睡眠数据.