BM-BPW:一种基于Mamba的双向模型,用于在心律失常中估计血压波形
概括
这项研究介绍了BM-BPW,这是一种新的深度学习模型,用于使用心电图和PPG信号在心律失常患者中持续监测血压 (BP). 该模型实现了高精度,超过了现有的无袖BP估计方法.
科学领域:
- 生物医学工程 生物医学工程
- 心血管技术的心血管技术
- 医疗保健中的人工智能
背景情况:
- 持续的血压 (BP) 监测对于管理心律失常的患者至关重要.
- 使用心电图 (ECG) 和光电图 (PPG) 信号与深度学习的无袖血压测量显示出有希望.
- 现有的深度学习模型与位置敏感的生理数据和全球背景作斗争,限制了准确性.
研究的目的:
- 引入BM-BPW,这是一个新的深度学习模型,用于从ECG和PPG信号中持续估计BP波形.
- 为了应对位置敏感性和全球背景在心律失常患者血压监测的挑战.
- 提高无袖血压监测在这个群体中的准确性和效率.
主要方法:
- 开发了BM-BPW模型,采用双向Mamba架构,并为全球背景和本地意识嵌入位置.
- 集成了一个多尺度卷积神经网络 (CNN) 功能提取器,以捕获当地的生理特征.
- 在48名心律失常患者的36537次心跳数据集上评估模型,使用侵入性动脉波形作为参考标准.
主要成果:
- 在心律失常患者中,BM-BPW有效地管理了BP波形变化.
- 取得的低估值误差:0.04 ± 5.03 mmHg对于静缩血压 (SBP) 和-0.05 ± 3.20 mmHg对于腹缩血压 (DBP).
- 与最先进的方法相比,在无袖式BP估计中表现出优异的性能.
结论:
- 在心律失常患者中,BM-BPW提供了一个准确有效的解决方案,用于无袖连续的BP波形监测.
- 该模型能够处理由心律失常引起的血压变化是显著的进步.
- 这项技术具有很大的潜力,可以改善心律失常患者的临床管理和患者的治疗结果.
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