Physiowise: A Physics-aware Approach to Dicrotic Notch Identification.
Physiowise: A Physics-aware Approach to Dicrotic Notch Identification.
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Physiowise：重搏切迹识别的物理感知方法。
DOI：
10.1145/3578556
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Ghiasi,Soheil
中科院分区：
文献类型：
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作者：
Saffarpour,Mahya;Basu,Debraj;Radaei,Fatemeh;Vali,Kourosh;Adams,JasonY;Chuah,Chen-Nee;Ghiasi,Soheil
重搏切迹（DN）是动脉血压（ABP）波形的最显著和指示性特征之一，变得不太明显，因此更难识别为老化和病理性血管僵硬的问题。在存在由于内部和外部噪声源或导致血液动力学不稳定的病理条件而发生的意外ABP波形变形的情况下，针对这种边缘情况的可推广和自动DN识别甚至更具挑战性。我们提出了一种物理感知的方法，命名为Physiowise（PW），首先采用心血管模型来增强原始ABP波形并减少意外变形，然后在增强信号上应用一组预定义的规则来找到DN位置。我们已经测试了所提出的方法在体内收集的数据，从14头猪出血和败血症的研究。我们的研究结果表明，52%的总体平均误差改善与16%的检测精度更高的最低允许误差范围内的30毫秒。一个额外的混合方法，还提出了允许结合增强与任何特定于应用程序的用户定义的规则集。
Dicrotic Notch (DN), one of the most significant and indicative features of the arterial blood pressure (ABP) waveform, becomes less pronounced and thus harder to identify as a matter of aging and pathological vascular stiffness. Generalizable and automatic DN identification for such edge cases is even more challenging in the presence of unexpected ABP waveform deformations that happen due to internal and external noise sources or pathological conditions that cause hemodynamic instability. We propose a physics-aware approach, named Physiowise (PW), that first employs a cardiovascular model to augment the original ABP waveform and reduce unexpected deformations, then apply a set of predefined rules on the augmented signal to find DN locations. We have tested the proposed method on in-vivo data gathered from 14 pigs under hemorrhage and sepsis study. Our result indicates 52% overall mean error improvement with 16% higher detection accuracy within the lowest permitted error range of 30 ms. An additional hybrid methodology is also proposed to allow combining augmentation with any application-specific user-defined rule set.