The underlying mechanism of intersite discrepancies in ejection time measurements from arterial waveforms and its validation in the Framingham Heart Study

A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning

Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable healthcare electronics. To assess the central aortic pressure using radial-based measurements, there is an essential need to develop mathematical approaches to estimate the central pressure waveform. In this study, we propose a new Fourier-based machine learning (F-ML) methodology to transfer non-invasive radial pressure measurements to the central pressure waveform. To test the method, collection of tonometry recordings of the radial and carotid pressure measurements are used from the Framingham Heart Study (2640 individuals, 55 % women) with mean (range) age of 66 (40-91) years. Method-derived estimates are significantly correlated with the measured ones for three major features of the pressure waveform (systolic blood pressure, r=0.97, p < 0.001; diastolic blood pressure, r=0.99, p < 0.001; and mean blood pressure, r=0.99, p < 0.001). In all cases, the Bland-Altman analysis shows negligible bias in the estimations and error is bounded to 5.4 mmHg. Findings also suggest that the F-ML approach reconstructs the shape of the central pressure waveform accurately with the average normalized root mean square error of 5.5 % in the testing population which is blinded to all stages of machine learning development. The results show that the F-ML transfer function outperforms the conventional generalized transfer function, particularly in terms of reconstructing the shape of the central pressure waveform morphology. The proposed F-ML transfer function can provide accurate estimates for the central pressure waveform, and ultimately expand the usage of non-invasive devices for central hemodynamic assessment.

Cross-Channel Dynamic Weighting RPCA: A De-Noising Algorithm for Multi-Channel Arterial Pulse Signal

Pulse wave analysis (PWA) has been widely used in the medical field. A novel multi-channel sensor is employed in arterial pulse acquisition and brings richer physiological information to PWA. However, the noise of this sensor is distributed in the main frequency band of the pulse signal, which seriously interferes with subsequent analyses and is difficult to eliminate by existing methods. This study proposes a cross-channel dynamic weighting robust principal component analysis algorithm. A channel-scaled factor technique is used to manipulate the weighting factors in the nuclear norm. This factor can adaptively adjust the weights among the channels according to the signal pattern of each channel, optimizing the feature extraction in multi-channel signals. A series of performance evaluations were conducted, and four well-known de-noising algorithms were used for comparison. The results reveal that the proposed algorithm achieved one of the best de-noising performances in the time and frequency domains. The mean of h1 in the amplitude relative error (ARE) was 23.4% smaller than for the WRPCA algorithm. Moreover, our algorithm could accelerate convergence and reduce the computational time complexity by approximately 34.6%. These results demonstrate the performance and efficiency of the algorithm. Meanwhile, the idea can be extended to other multi-channel physiological signal de-noising and feature extraction fields.

Accuracy and applicability of non-invasive evaluation of aortic wave intensity using only pressure waveforms in humans

Background.Wave intensity (WI) analysis is a well-established method for quantifying the energy carried in arterial waves, providing valuable clinical information about cardiovascular function. The primary drawback of this method is the need for concurrent measurements of both pressure and flow waveforms.Objective. We have for the first time investigated the accuracy of a novel methodology for estimating wave intensity employing only single pressure waveform measurements; we studied both carotid- and radial-based estimations in a large heterogeneous cohort.Approach.Tonometry was performed alongside Doppler ultrasound to acquire measurements of both carotid and radial pressure waveforms as well as aortic flow waveforms in 2640 healthy and diseased participants (1439 female) in the Framingham Heart Study. Patterns consisting of two forward waves (Wf1, Wf2) and one backward wave (Wb1) along with reflection metrics were compared with those obtained from exact WI analysis.Main Results. Carotid-based estimates correlated well for forward peak amplitudes (Wf1,r = 0.85,p < 0.05; Wf2,r = 0.72,p < 0.05) and peak time (Wf1,r = 0.94,p < 0.05; Wf2,r = 0.98,p < 0.05), and radial-based estimates correlated fairly to poorly for amplitudes (Wf1,r = 0.62,p < 0.05; Wf2,r = 0.42,p < 0.05) and peak time (Wf1,r = 0.04,p = 0.10; Wf2,r = 0.75,p < 0.05). In all cases, estimated Wb1 measures were not correlated. Reflection metrics were well correlated for healthy patients (r = 0.67,p < 0.05), moderately correlated for valvular disease (r = 0.59,p < 0.05) and fairly correlated for CVD (r = 0.46,p < 0.05) and heart failure (r = 0.49,p < 0.05).Significance. These findings indicate that pressure-only WI produces accurate results only when forward contributions are of primary interest and only for carotid pressure waveforms. The pressure-only WI estimations of this work provide an important opportunity to further the goal of uncovering clinical insights through wave analysis affordably and non-invasively.