睡眠研究中的无监督机器学习:一个范围审查
Luka Biedebach1,2, Daniela Ferreira-Santos3,4, Marie-Ange Stefanos5
1Department of Computer Science, Reykjavik University, Iceland.
Sleep
|July 28, 2025
概括
无监督机器学习在睡眠研究中越来越多地使用,集群是常见的方法. 本综述绘制了当前应用的地图,并确定了睡眠研究的未来研究方向.
科学领域:
- 睡眠科学 睡眠科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 无监督机器学习 (ML) 在没有标签的数据中突出发现模式.
- 它在睡眠研究中的成功凸显了新的见解的更广泛潜力.
- 对于更先进的睡眠研究,需要进一步探索.
研究的目的:
- 在睡眠研究中对无监督ML进行范围审查.
- 绘制现有文献的地图,并确定研究缺口.
- 提出未来的研究方向.
主要方法:
- 根据PRISMA指南进行范围审查.
- 综合的文献搜索产生了3960个出版物.
- 全文审查356个选定的出版物.
主要成果:
- 在过去的十年中,出版物的数量大幅增加.
- 聚类是主要的无监督ML方法.
- 使用多种数据源,包括可穿戴设备,视频,音频和医学成像.
- 应用范围涵盖一般和临床睡眠人群.
结论:
- 该审查提供了一个全面的概述无监督的ML在睡眠.
- 识别了文献中的空白,以供未来的研究.
- 概述了利用无监督ML推进睡眠研究的关键方向.
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