通过结合深度学习和校准的光电显微镜记录改进了高血压风险评估
Jesús Cano1, Vicente Bertomeu-González2, Lorenzo Fácila3
1BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain.
Bioengineering (Basel, Switzerland)
|December 23, 2023
概括
深度学习模型准确地检测高血压使用光聚血学 (PPG) 信号. 密切的校准间隔对于可靠的血压监测至关重要,随着间隔的延长,性能会下降.
科学领域:
- 心血管疾病的研究研究.
- 医疗保健中的人工智能
- 生物医学信号处理
背景情况:
- 高血压是心血管疾病的主要全球健康风险因素.
- 早期发现和管理高血压对于减轻健康风险至关重要.
- 光电显微镜 (PPG) 为生理监测提供了一种非侵入性方法.
研究的目的:
- 评估深度学习 (DL) 模型 (GoogLeNet,ResNet-18,ResNet-50) 用于使用PPG信号对正常压力 (NTS) 和高压 (HTS) 个体进行分类.
- 评估不同校准时间间隔对基于DL的高血压检测准确性的影响.
- 确定最佳的校准频率,以可靠的PPG血压评估.
主要方法:
- 使用了三种深度学习架构:googlenet,resnet18和resnet50.
- 采用光电显微镜 (PPG) 记录用于特征提取.
- 在不同的校准间隔中研究的分类性能:<1小时,1-6小时,6-24小时和>24小时.
主要成果:
- 所有测试的DL模型都实现了90%以上的准确性,校准距离很近 (<1小时).
- ResNet-18表现出卓越的性能,准确度为93.32%,灵敏度为84.09%,特异性为97.30%,F1评分为88.36%,间隔小于1小时.
- 随着校准间隔的延长,分类准确性下降,在 > 6 h 的间隔下降到 81% 以下,但在 > 24 h 的间隔下保持在 71% 以上.
结论:
- 深度学习模型,特别是ResNet-18,显示出使用PPG信号精确检测高血压的巨大潜力.
- 频繁的校准 (间隔小于1小时) 对于保持基于PPG的高血压监测中高分类准确度至关重要.
- 这些发现支持开发非侵入性,实时和可靠的血压监测系统.
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