睡眠-深度-学习者被最终用户教导睡眠-觉醒得分,以他们的风格完成每个记录
Fumi Katsuki1, Tristan J Spratt1, Ritchie E Brown1
1Department of Psychiatry, VA Boston Healthcare System and Harvard Medical School, West Roxbury, MA, USA.
Sleep advances : a journal of the Sleep Research Society
|April 19, 2024
概括
这项研究介绍了Sleep-Deep-Learner,这是一种人工智能工具,通过使用定制的神经网络,显著加快动物睡眠得分. 它准确地分析了脑电图 (EEG) 和局部场电位 (LFP) 数据,减少了手工工作的12倍.
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
- 睡眠研究 睡眠研究
背景情况:
- 在动物中手动睡眠-觉醒评分是必不可少的,但由于微妙的EEG振幅差异和快速状态转换,耗时且具有挑战性.
- 现有的自动化动物睡眠评分方法往往无法对新数据集进行概括,特别是那些经过改变的EEG/EMG配置文件的数据集.
- 专家手工评分仍然是黄金标准,尽管它的劳动密集型性质.
研究的目的:
- 开发一种人工智能驱动的方法,睡眠深度学习,以加速动物的专家睡眠评分.
- 使用转移学习为个人EEG或LFP记录创建定制的深卷积神经网络模型.
- 将大多数动物睡眠数据的得分自动化,利用用户提供的手动得分的小部分.
主要方法:
- 开发了Sleep-Deep-Learner,这是一个利用GoogleLeNet转移学习的系统,用于构建个性化的深卷积神经网络模型.
- 实施了一种新的REM睡眠评分校正程序,以提高整体准确性.
- 训练模型在手动评分的EEG/LFP数据的小子集上,为每个单独的记录.
主要成果:
- 睡眠深度学习器可靠地评分EEG和LFP数据,在各种条件 (野生类型小鼠,索尔皮诱导的睡眠,阿尔茨海默病模型,遗传敲击) 中保存睡眠-觉醒架构.
- 显著减少了手动得分时间的12倍.
- 在因药物,疾病模型或遗传修饰而改变信号配置的数据集上表现出强大的性能,因为它避免了从先前存在的数据集中产生偏差.
结论:
- 睡眠深度学习器有效地加快了动物的睡眠得分,同时与手动得分相比保持了高准确度.
- 转移学习方法允许模型适应单个记录,确保在各种实验条件下可靠的性能.
- 这种人工智能工具为需要在临床前研究中高效准确的睡眠分析的研究人员提供了有价值的解决方案.
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