睡眠自动分期
基于统计规则的睡眠分期 > 总结各个模型表现

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| 文献 | 文献类型 | 数据库 | 预处理 | 特征提取 | 分类器 | 准确率(%) |
| 1  | 中文 | Sleep-EDF | × | 样本熵 | 相关向量机 | 89.0 |
| 2  | 中文 | Sleep-EDF | 滤波 | 主成分分析 | 支持向量机 | 87.1 |
| 3  | 中文 | Sleep-EDF | 滤波 | 多尺度熵 | Bp网络 | 85.8 |
| 4  | 中文 | Sleep-EDF | 滤波 | 精细多尺度熵 | 支持向量机 | 85.3 |
| 5  | 中文 | Sleep-EDF | × | 模糊熵 | 支持向量机 | 83.0 |
| 6  | 英文 | 海德堡大学 | 归一化、基线漂移 | Hilbert-Huang Transform | 随机森林 | 83.0 |
| 7  | 英文 | Sleep-EDF | × | 时频 | 自编码器 | 78.9 |
| 8  | 英文 | ISRUC-Sleep | × | 时频等 | softmax | 74.9 |
| 9  | 英文 | ISRUC-Sleep | × | 时频等 | K-NN | 73.3 |
| 10  | 英文 | 圣保罗大学 | × | 小波 | 支持向量机 | 72.3 |
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专家人工睡眠分期过程
R&K睡眠分期标准和AASM睡眠分期标准关系图
卷积神经网络示意图
递归神经网络图

RVM睡眠分期二叉式模型