Vargas Juan M, Bahloul Mohamed A, Laleg-Kirati Taous-Meriem
Computer, Electrical, and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Makkah, Saudi Arabia.
Electrical Engineering Department, Alfaisal University, Riyadh, Saudi Arabia.
Front Physiol. 2023 Mar 3;14:1100570. doi: 10.3389/fphys.2023.1100570. eCollection 2023.
Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law's mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R\geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R\geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for signals.
颈股脉搏波速度（cf-PWV）被认为是评估动脉僵硬度的关键指标。因此，估计颈股脉搏波速度（cf-PWV）对于诊断和分析不同的心血管疾病至关重要。尽管其在临床常规中得到了更广泛的应用，但颈股脉搏波速度的测量过程对临床医生和患者来说是一项艰巨的任务，这使得其在估计中容易出现不准确和错误。一种智能的、非侵入性的外周颈股脉搏波速度测量方法可以克服传统评估过程中的挑战，并提高患者护理质量。本文提出了一种基于使用来自单个非侵入性外周脉搏波信号[光电容积脉搏波描记法（PPG）或血压（BP）]的频谱图表示来估计颈股脉搏波速度的新方法。该方法使用了基于半经典信号分析（SCSA）方法、用于纹理能量提取的劳氏模板和中心统计矩的三种特征提取方法进行了测试。最后，将每种特征方法输入到不同的机器学习模型中进行颈股脉搏波速度估计。对于无噪声情况，所提出的方法使用MLP模型对所有外周信号获得了$R\geq0.90$，对于添加到原始信号的不同噪声水平，基于SCSA的特征与MLP模型在噪声水平下对所有外周信号呈现出$R\geq0.91$。这些结果证明了频谱图表示使用不同特征方法有效评估颈股脉搏波速度估计的能力。未来的工作将朝着测试所提出的方法用于信号展开。
