Non-Invasive Pressure-Only Aortic Wave Intensity Evaluation Using Hybrid Fourier Decomposition-Machine Learning Approach.

Abstract

The clinical significance of the wave intensity (WI) analysis for the diagnosis and prognosis of the cardiovascular and cerebrovascular diseases is well-established. However, this method has not been fully translated into clinical practice. From practical point of view, the main limitation of WI method is the need for concurrent measurements of both pressure and flow waveforms. To overcome this limitation, we developed a Fourier-based machine learning (F-ML) approach to evaluate WI using only the pressure waveform measurement. Tonometry recordings of the carotid pressure and ultrasound measurements for the aortic flow waveforms from the Framingham Heart Study (2640 individuals; 55% women) were used for developing the F-ML model and the blind testing. Method-derived estimates are significantly correlated for the first and second forward wave peak amplitudes (Wf1, r = 0.88, p 0.05; Wf2, r = 0.84, p 0.05) and the corresponding peak times (Wf1, r = 0.80, p < 0.05; Wf2, r = 0.97, p 0.05). For backward components of WI (Wb1), F-ML estimates correlated strongly for the amplitude (r = 0.71, p 0.05) and moderately for the peak time (r = 0.60, p 0.05). The results show that the pressure-only F-ML model significantly outperforms the analytical pressure-only approach based on the reservoir model. In all cases, the Bland-Altman analysis shows negligible bias in the estimations. The proposed pressure-only F-ML approach provides accurate estimates for WI parameters. The pressure only F-ML approach introduced in this work expand the clinical usage of WI into inexpensive and non-invasive settings such as wearable telemedicine.