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

ABSTRACT: 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 b

SUBMITTER: Tamborini A 

PROVIDER: S-EPMC11892652 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature