Saffarpour Mahya, Basu Debraj, Radaei Fatemeh, Vali Kourosh, Adams Jason Y, Chuah Chen-Nee, Ghiasi Soheil
Department of Electrical and Computer Engineering, UC Davis.
Department of Pulmonary and Critical Care Medicine, UC Davis School of Medicine.
ACM Trans Comput Healthc. 2023 Apr;4(2). doi: 10.1145/3578556. Epub 2023 Apr 18.
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. An additional hybrid methodology is also proposed to allow combining augmentation with any application-specific user-defined rule set.
重搏波切迹（DN）是动脉血压（ABP）波形最重要且最具指示性的特征之一，随着年龄增长和病理性血管僵硬，它会变得不那么明显，因此更难识别。在存在由于内部和外部噪声源或导致血流动力学不稳定的病理状况而发生的意外ABP波形变形的情况下，针对此类边缘情况进行通用且自动的DN识别更具挑战性。我们提出了一种名为Physiowise（PW）的物理感知方法，该方法首先采用心血管模型来增强原始ABP波形并减少意外变形，然后对增强后的信号应用一组预定义规则来找到DN位置。我们已经在从14头猪身上收集的出血和脓毒症研究的体内数据上测试了所提出的方法。我们的结果表明，在最低允许误差范围30内，总体平均误差提高了52%，检测准确率提高了16%。还提出了一种额外的混合方法，允许将增强与任何特定应用的用户定义规则集相结合。
