从倾斜表测试上的生理信号对血管的分类
Mahbuba Ferdowsi1,2, Ban-Hoe Kwan1,2, Maw Pin Tan3
1Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000, Kajang, Malaysia.
Biomedical engineering online
|March 30, 2024
概括
这项研究开发了一个算法,使用头向上倾斜测试中的血压和心电图数据来准确地分类血管 (VVS) 患者. 机器学习模型达到90%以上的准确性,提供了一个有前途的诊断工具.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 血管 (VVS) 是昏的常见原因,通常使用头向上倾斜测试 (HUTT) 进行诊断.
- HUTT涉及在姿势变化期间监测血压 (BP) 和心电图 (ECG) 等生理反应.
- 在测试期间,患者可能会出现恶心,出汗,白,心和昏迷等症状.
研究的目的:
- 开发和评估用于对VVS患者进行分类的机器学习算法.
- 在HUTT期间获得的生理信号 (BP和ECG) 用于VVS诊断.
- 为了确定准确的VVS分类的关键特征.
主要方法:
- 试验对象接受了70度的头向上倾斜测试,并给予甘三酸盐.
- 缺少的数据使用K-最近邻居 (KNN) 归算来处理.
- 使用特征选择技术和机器学习模型 (SVM,GNB,MNB,KNN,LR,RF) 来进行分类.
- 部分依赖图用于模型解释.
主要成果:
- 该研究包括137名受试者 (54名VVS阳性,83名阴性).
- 结合KNN赋值,三个倾斜特征和支持矢量机 (SVM) 的最佳模型实现了90.5%的准确性.
- 性能指标包括87.0%的灵敏度,92.7%的特异性,88.6%的精度,87.8%的F1得分和95.4%的ROC AUC.
结论:
- 开发的算法有效地对VVS患者进行分类,准确度高 (>90%).
- 这些发现表明,使用生理信号和机器学习来改善VVS诊断的潜力.
- 建议对更大的临床数据集进行进一步验证,以确保可通用性.
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