ArticleProceedings of the National Academy of Sciences of the United States of America2025
A spectral machine learning approach to derive central aortic pressure waveforms from a brachial cuff.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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3 authors.
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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 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 pressure (BP) oscillations (r = 0.76,
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