从自然语音声音中早期检测高血压,使用图形扩散网络
Haydar Ankışhan1, Haydar Celik2, Haluk Ulucanlar3
1Stem Cell Institute, Ankara University, Ankara, Turkey.
Computers in biology and medicine
|December 24, 2024
概括
这项研究引入了一种使用语音和人口统计数据的无袖血压预测方法. 图形扩散网络 (GDN) 模型显示了早期高血压检测的高准确性,提供了一个非侵入性监测解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 心血管健康 心血管健康
背景情况:
- 传统的血压监测需要手,限制了连续和远程评估.
- 非侵入性方法对于广泛的查和管理至关重要,特别是在服务不足的地区.
研究的目的:
- 开发和验证使用语音和人口特征的无袖血压预测模型.
- 评估模型在检测早期高血压的有效性.
主要方法:
- 利用图形扩散网络 (GDN) 模型整合语音信号和人口统计数据.
- 使用R平方 (R2) 和皮尔森相关系数 (PCC) 评估模型性能.
- 使用F1-Score和准确度指标评估了早期高血压检测.
主要成果:
- 在结合语音和人口特征时,GDN模型实现了0.96的R2和0.98的PCC.
- 对于早期高血压检测,该模型的F1-Score为0.8735±0.10,准确度为0.8896±0.11.
- 仅仅语音特征就产生了0.740的R2和0.764的PCC,证明了人口统计数据的附加值.
结论:
- 结合语音和人口特征,显著提高了无袖血压预测的准确性.
- 开发的模型显示出非侵入性,远程血压监测和早期高血压检测的巨大潜力.
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