人工智能在打声音分析:OSA检测和障碍站点分类,一个系统审查
Francesco Carlo Tartaglia1, Gian Marco Pace2,3, Francesco Giombi3
1Department of Biomedical Sciences, Humanitas University, Milan, Italy.
概括
机器学习和人工智能显示出通过打声音分析检测阻塞性睡眠呼吸暂停 (OSA) 的前景. 这些人工智能模型还可以对阻塞部位进行分类,有助于非侵入性诊断和治疗规划.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 睡眠医学 睡眠医学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSA) 是一种普遍的睡眠障碍.
- 准确检测和分类OSA对于有效管理至关重要.
- 打的声音为OSA诊断提供了一个潜在的非侵入性生物标志物.
研究的目的:
- 系统地审查机器学习 (ML) 和人工智能 (AI) 在分析打声音中的应用.
- 评估ML/AI模型在检测OSA中的有效性.
- 评估这些模型在使用毛皮,耳鼻,舌头和上管 (VOTE) 系统对上呼吸道阻塞部位进行分类的能力.
主要方法:
- 在主要数据库 (PubMed,Scopus等) 进行了全面的文献搜索. 从2011年开始到2024年11月.
- 根据PRISMA 2020指南进行了选和审查,包括42项研究.
- 使用PROBAST评估方法质量,重点关注ML模型,数据集,特征提取和性能指标.
主要成果:
- 机器学习模型,包括SVM和CNN,在OSA检测和打分类方面实现了高精度 (高达98.6%).
- 确定了MFCC和波形变换等关键声学特征.
- VOTE分类的性能各不相同,有针对性的特征在一些研究中提高了准确度超过95%,尽管数据不平衡和噪音等挑战仍然存在.
结论:
- 人工智能驱动的打分析为OSA查和解剖部位分类提供了一种可行的非侵入性方法.
- 进一步的研究应该强调多式联运数据集成和临床翻译的现实世界验证.
- 提高模型通用性对于在临床实践中得到广泛采用至关重要.
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