基于卷积神经网络的睡眠的分类和转移学习
Jun Liang1, Abdelkader Nasreddine Belkacem2, Yanxin Song3,4
1Department of Rehabilitation Medicine, Tianjin Medical University General Hospital, Tianjin, China.
Frontiers in neuroscience
|May 9, 2024
概括
一个新的卷积神经网络 (CNN) 有效地使用转移学习对睡眠进行分类. 来自健康受试者的特征改善了失眠患者的分类,有助于诊断睡眠障碍.
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
- 医疗信息学 医疗信息学
背景情况:
- 睡眠对健康至关重要,脑电图 (EEG) 对于诊断睡眠障碍至关重要.
- 睡眠,一个关键的EEG现象,在睡眠科学和诊断中很重要.
- 准确识别睡眠,有助于理解睡眠模式和诊断疾病.
研究的目的:
- 提出一种新的卷积神经网络 (CNN) 模型来对睡眠进行分类.
- 调查转移学习的有效性,用于将健康人体上训练的模型应用于失眠患者.
- 分析不同数量的卷积层转移对分类性能的影响.
主要方法:
- 开发了一种新的CNN模型,用于睡眠线圈分类.
- 员工转移学习以适应在健康受试者身上训练的模型来分类失眠患者的.
- 评估分类性能与卷积层的部分和完全转移.
主要成果:
- 在健康 (93.68%) 和失眠 (92.77%) 个体的睡眠线索中,CNN模型实现了高分类准确性.
- 转移学习,特别是转移前四个卷积层,在失眠受试者中产生了强有力的结果 (92.80%的准确性).
- 证明从健康受试者的数据中学习的特征可以有效地转移到失眠受试者的数据中.
结论:
- 拟议的CNN模型对于分类睡眠是有效的.
- 转移学习是改善失眠患者睡眠线圈分类的可行策略.
- 该模型显示了快速有效诊断和治疗睡眠障碍的潜力.
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