电图和声图信号的同步获取和处理,以准确测量心缩时间,用于心脏病诊断和监测
Roberto De Fazio1,2, Ilaria Cascella1, Şule Esma Yalçınkaya1
1Department of Innovation Engineering, University of Salento, Road to Monteroni, Building 'O', 73100 Lecce, Italy.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种结合心电图 (ECG) 和心声图 (PCG) 的新方法,以更好地监测心血管疾病. 开发的算法准确地提取了关键的心脏参数,提高了诊断潜力.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 信号处理 信号处理
背景情况:
- 心血管疾病是全球死亡的主要原因之一.
- 电心电图 (ECG) 是心脏电活动的标准,但对于像门疾病这样的疾病是不够的.
- 声心电图 (PCG) 分析心脏声音,在与心电图相结合时提高诊断准确性.
研究的目的:
- 开发和验证用于同时采集和分析ECG和PCG信号的方法.
- 为心血管疾病监测提取关键缩时间间隔 (EMAT,PEP,LVET,LVST).
- 评估开发的自适应细分算法的可靠性和稳定性.
主要方法:
- 同时采集ECG和PCG信号,使用一个带有AD8232模拟前端和数字耳语镜的紧型系统.
- 在胸部放置心电图电极和麦克风,以实现空间信号对齐.
- 开发一种自适应细分算法,以识别关键波 (Q,R,S) 和心声 (S1,S2) 进行参数提取.
主要成果:
- 测量的平均缩时间间隔 (EMAT,PEP,LVET,LVST) 与参考标准一致.
- 该算法在不同记录条件下表现出稳定性,提取的间隔与文献值密切匹配.
- 在BSSLAB本地化ECG数据数据集上的验证证实了该方法的可靠性.
结论:
- 联合ECG和PCG分析显示了改善长期心血管疾病监测的巨大潜力.
- 开发的自适应细分算法可以准确地提取重要心血管参数.
- 这种综合方法为改善心脏诊断和患者管理提供了有前途的工具.
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