对失眠的自动识别进行系统审查
Manisha Ingle1, Manish Sharma2, Kamlesh Kumar3
1VNIT, nagpur, Nagpur, Maharashtra, 440010, INDIA.
Physiological measurement
|January 18, 2024
概括
这项研究审查了用于自动检测失眠的机器学习和深度学习算法. 虽然目前的人工智能显示出希望,但需要进一步的进展,以提高识别这种睡眠障碍的准确性和可靠性.
科学领域:
- 睡眠医学 睡眠医学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 失眠是一种常见的睡眠障碍,影响睡眠的数量和质量.
- 机器学习 (ML) 和深度学习 (DL) 为自动化睡眠分析提供了先进的工具.
- 生理信号是检测失眠等睡眠障碍的关键数据来源.
研究的目的:
- 探索和分类用于自动检测失眠的算法.
- 为了比较各种ML和DL技术在识别失眠方面的性能.
- 确定在自动化失眠检测方面的研究缺口和未来机会.
主要方法:
- 根据PRISMA指南 (2015-2023) 进行系统审查.
- 分析了30多篇出版物,重点是自动识别失眠.
- 在注释的生理信号上训练的ML/DL模型的评估,确定了15个不同的算法.
主要成果:
- 自动失眠检测系统利用各种数据源,ML/DL网络和培训框架.
- 基于ML/DL模型的观点,学习结构和输入数据类型的研究分类.
- 确定了自动化技术的关键组成部分:数据输入,目标,AI模型,培训和数据库.
结论:
- 目前的自动化失眠检测方法有希望,但需要提高准确性和可靠性.
- 存在重大研究差距,突出了未来技术和算法进步的机会.
- 人工智能的进一步发展可以导致更有效和高效的失眠识别.
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