A spectral machine learning approach to derive central aortic pressure waveforms from a brachial cuff

• 成果类型: Article
• 署名作者: Tamborini, Alessio; Aghilinejad, Arian; Gharib, Morteza
• 署名单位: California Institute of Technology
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11995
• DOI: 10.1073/pnas.2416006122
• 发表日期: 2025-02-28
• 关键词: blood-pressure; cardiovascular events; pulse pressure; upper-limb; validation; disease; amplification; components; exercise; MODEL

摘要

Analyzing cardiac pulse waveforms offers valuable insights into heart health and cardiovascular disease risk, although obtaining the more informative measurements from the central aorta remains challenging due to their invasive nature and limited noninvasive options. To address this, we employed a laboratory- developed cuff device for high- resolution pulse waveform acquisition and constructed a spectral machine learning model to nonlinearly map the brachial wave components to the aortic site. Simultaneous invasive aortic catheter and brachial cuff waveforms were acquired in 115 subjects to evaluate the clinical performance of the developed wave- based approach. Magnitude, shape, and pulse waveform analysis on the measured and reconstructed aortic waveforms were correlated on a beat- to- beat basis. The proposed cuff- based method reconstructed aortic waveform contours with high fidelity (mean normalized- RMS error = 11.3%). Furthermore, continuous signal reconstruction captured dynamic aortic systolic blood 0.83; B [LOA] = -0.3 [-17.0, 16.4] mmHg) and diastolic BP (R2 = 0.58; B [LOA] = machine learning method, showing strong correlations and no systemic bias for systolic that the nonlinear transformation of wave components from the distal to the central site predicts the morphological waveform changes resulting from complex wave propagation holds promise for future applications of noninvasive devices in clinical cardiology.