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A spectral machine learning approach to derive central aortic pressure waveforms from a brachial cuff
Alessio Tamborini1, Arian Aghilinejad1, Morteza Gharib1
1Department of Medical Engineering, California Institute of Technology, Pasadena, CA 91125.
Insights
A new cuff device and machine learning model accurately reconstruct central aortic pulse waveforms noninvasively. This method shows high fidelity and strong correlations for blood pressure and cardiac viability, promising for clinical cardiology.
Area of Science:
- Cardiovascular Physiology
- Biomedical Engineering
- Machine Learning in Medicine
Background:
- Central aortic pulse waveforms provide critical cardiovascular insights but are difficult to obtain noninvasively.
- Current methods for central pressure measurement are often invasive or lack precision.
Purpose of the Study:
- To develop and validate a noninvasive method for reconstructing central aortic pulse waveforms using brachial artery measurements.
- To assess the clinical performance of a spectral machine learning model for predicting central hemodynamics.
Main Methods:
- A laboratory-developed cuff device acquired high-resolution brachial pulse waveforms.
- A spectral machine learning model nonlinearly mapped brachial wave components to the aortic site.
- Simultaneous invasive aortic catheter and brachial cuff waveforms were collected from 115 subjects.
Main Results:
- The noninvasive method reconstructed aortic waveforms with high fidelity (11.3% error).
- It accurately captured dynamic systolic blood pressure oscillations (r=0.76) and correlated well with invasive measurements (systolic BP R²=0.83, diastolic BP R²=0.58).
- Shape-based features, including pressure-time integrals and subendocardial viability ratio, showed strong correlations (r=0.86-0.95).
Conclusions:
- The proposed wave-based approach effectively predicts central aortic waveform morphology noninvasively.
- This technique holds significant promise for advancing noninvasive cardiovascular diagnostics in clinical cardiology.
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, P < 0.05). Method-derived central pressures showed strong correlation with the independent invasive measurement for systolic BP (R2 = 0.83; B [LOA] = -0.3 [-17.0, 16.4] mmHg) and diastolic BP (R2 = 0.58; B [LOA] = -0.7 [-13.1, 11.6] mmHg). Shape-based features are effectively captured by the spectral machine learning method, showing strong correlations and no systemic bias for systolic pressure-time integral (r = 0.91, P < 0.05), diastolic pressure-time integral (r = 0.95, P < 0.05), and subendocardial viability ratio (r = 0.86, P < 0.05). These results suggest that the nonlinear transformation of wave components from the distal to the central site predicts the morphological waveform changes resulting from complex wave propagation and reflection within the cardiovascular network. The proposed wave-based approach holds promise for future applications of noninvasive devices in clinical cardiology.
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