FlexSleepTransformer:一个基于变压器的睡眠阶段模型,具有灵活的输入通道配置.
Yanchen Guo1, Maciej Nowakowski2, Weiying Dai3
1School of Computing, State University of New York at Binghamton, Binghamton, NY, 13902, USA.
Scientific reports
|November 2, 2024
概括
一个新的深度学习模型FlexSleepTransformer使用灵活的多睡眠学 (PSG) 通道自动化睡眠阶段分类. 它在各种睡眠数据集中实现了高精度和适应性,为临床集成铺平了道路.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
背景情况:
- 临床睡眠诊断依赖于多睡眠学 (PSG) 和手动睡眠阶段分类.
- 深度学习模型显示了自动睡眠分期的前景,但在不同睡眠中心的不同PSG通道数量方面存在困难.
- 需要灵活的方法来将自动睡眠分期集成到不同的临床环境中.
研究的目的:
- 开发和评估FlexSleepTransformer,这是一个基于变压器的模型,用于自动化睡眠阶段分类,可适应可变数量的PSG通道.
- 在不同道配置的异质数据集上训练时评估模型的性能.
- 将FlexSleepTransformer与现有的最先进的模型进行比较.
主要方法:
- 提出了FlexSleepTransformer,这是一个基于变压器的深度学习架构,旨在实现可变输入通道灵活性.
- 在两个不同的数据集上训练和评估模型:SleepEDF-78和SleepUHS,PSG频道数量不同.
- 进行实验,以评估不同频道数和跨数据集性能的数据集的同时训练.
主要成果:
- 在同时对两个数据集进行训练时,FlexSleepTransformer实现了单独训练的模型98%的准确性.
- 该模型在测试其他数据集时,在单个数据集上训练的模型中表现优于模型.
- 在这两个数据集上,FlexSleepTransformer超越了最先进的卷积神经网络 (CNN) 和循环神经网络 (RNN) 模型.
结论:
- FlexSleepTransformer表现出强大的性能和适应性,适应不同的PSG通道数,这是临床整合的关键因素.
- 该模型在各种数据集上进行训练的能力提高了其在睡眠医学中广泛采用临床应用的潜力.
- 这种基于变压器的方法为自动化,灵活和准确的睡眠分期提供了一个有希望的解决方案.
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