Estimation of central blood pressure waveform from femoral blood pressure waveform by blind sources separation

Background Central blood pressure ( cBP ) is a better indicator of cardiovascular morbidity and mortality than peripheral BP ( pBP ). However, direct cBP measurement requires invasive techniques and indirect cBP measurement is based on rigid and empirical transfer functions applied to pBP . Thus, development of a personalized and well-validated method for non-invasive derivation of cBP from pBP is necessary to facilitate the clinical routine. The purpose of the present study was to develop a novel blind source separation tool to separate a single recording of pBP into their pressure waveforms composing its dynamics, to identify the compounds that lead to pressure waveform distortion at the periphery, and to estimate the cBP . The approach is patient-specific and extracts the underlying blind pressure waveforms in pBP without additional brachial cuff calibration or any a priori assumption on the arterial model. Methods The intra-arterial femoral BP fe and intra-aortic pressure BP ao were anonymized digital recordings from previous routine cardiac catheterizations of eight patients at the German Heart Centre Berlin. The underlying pressure waveforms in BP fe were extracted by the single-channel independent component analysis (SCICA). The accuracy of the SCICA model to estimate the whole c BP waveform was evaluated by the mean absolute error (MAE), the root mean square error (RMSE), the relative RMSE (RRMSE), and the intraclass correlation coefficient (ICC). The agreement between the intra-aortic and estimated parameters including systolic (SBP), diastolic (DBP), mean arterial pressure (MAP), and pulse pressure ( PP ) was evaluated by the regression and Bland–Altman analyses. Results The SCICA tool estimated the c BP waveform non-invasively from the intra-arterial BP fe with an MAE of 0.159 ± 1.629, an RMSE of 5.153 ± 0.957 mmHg, an RRMSE of 5.424 ± 1.304%, and an ICC of 0.94, as well as two waveforms contributing to morphological distortion at the femoral artery. The regression analysis showed a strong linear trend between the estimated and intra-aortic SBP, DBP, MAP, and PP with high coefficient of determination R 2 of 0.98, 0.99, 0.99, and 0.97 respectively. The Bland–Altman plots demonstrated good agreement between estimated and intra-aortic parameters with a mean error and a standard deviation of difference of −0.54 ± 2.42 mmHg 95% confidence interval (CI): −5.28 to 4.20 for SBP, −1.97 ± 1.62 mmHg (95% CI: −5.14 to 1.20) for DBP, −1.49 ± 1.40 mmHg (95% CI: −4.25 to 1.26) for MAP, and 1.43 ± 2.79 mmHg (95% CI: −4.03 to 6.90) for PP. Conclusions The SCICA approach is a powerful tool that identifies sources contributing to morphological distortion at peripheral arteries and estimates cBP .