多式心电图和生物识别数据融合,以改善阻塞性睡眠呼吸暂停症候群的检测
Quanjing Zhu1,2,3, Mingqing Liang4, Xingxin Gong1,2,3
1Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Frontiers in medicine
|February 13, 2026
概括
一种使用心电图和生物识别数据的新方法可以准确检测阻塞性睡眠呼吸暂停 (OSAHS) 综合征. 这种方法提供了一个具有成本效益和可靠的替代方案,用于OSAHS诊断传统的多睡眠学.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 睡眠医学 睡眠医学
背景情况:
- 阻塞性睡眠呼吸暂停 (OSAHS) 会导致严重的健康问题,如中风和认知能力下降.
- 目前通过多睡眠学 (PSG) 进行诊断是昂贵且难以获得的,导致诊断不足.
- 需要可负担得起和准确的OSAHS检测方法.
研究的目的:
- 开发一个低成本,准确的OSAHS检测系统.
- 使用心电图 (ECG) 信号和生物识别数据用于OSAHS识别.
- 为了提高OSAHS的诊断可访问性.
主要方法:
- 提出了一个多式联网功能融合框架.
- 使用长期短期记忆 (LSTM) 网络提取了心电图特征.
- 生物特征通过支持矢量机 (SVM) 处理,并用于OSAHS分类的融合.
主要成果:
- 在一个数据集上,LSTM-SVM融合模型实现了97.1%的准确性.
- 该模型在独立数据集上显示出强大的概括性,准确度为92%.
- 提出的方法优于传统的分类模型.
结论:
- 从LSTM衍生的心电图和SVM衍生的生物特征的多模式融合有效地检测OSAHS.
- 开发的方法显示了在OSAHS诊断中实际临床应用的巨大潜力.
- 这种方法为OSAHS查提供了一个有希望的,可访问的替代方案.
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