Sleep-Deep-Net从最终用户那里学习睡眠和觉醒的得分,并以他们的风格完成每个记录
Fumi Katsuki1, Tristan J Spratt1, Ritchie E Brown1
1VA Boston Healthcare System and Harvard Medical School, Dept. of Psychiatry, West Roxbury, MA 02132, USA.
bioRxiv : the preprint server for biology
|January 8, 2024
概括
在动物中手动进行睡眠评分是具有挑战性的. 睡眠深度网络 (SDN) 使用个性化的神经网络来加速专家评分,显著减少时间,同时在各种条件下保持准确性.
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
- 睡眠科学 睡眠科学
背景情况:
- 在动物中手动睡眠-觉醒得分是必不可少的,但耗时且劳动密集.
- 现有的自动化方法难以处理新的数据集,特别是那些具有改变的脑电图 (EEG) /脑电图 (EMG) 配置文件的数据集.
- 专家手动评分仍然是黄金标准,尽管存在挑战.
结论:
- SDN为动物睡眠评分提供了一种高效准确的解决方案.
- 个性化,基于转移学习的方法使得SDN对药物,疾病或基因修改具有强大影响.
- SDN加速了专家的评分,使其成为睡眠研究的宝贵工具.
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