Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study.

Abstract

Tracking changes in hemodynamic congestion and the consequent proactive readjustment of treatment has shown efficacy in reducing hospitalizations for patients with heart failure (HF). However, the cost-prohibitive nature of these invasive sensing systems precludes their usage in the large patient population affected by HF. The objective of this research is to estimate the changes in pulmonary artery mean pressure (PAM) and pulmonary capillary wedge pressure (PCWP) following vasodilator infusion during right heart catheterization (RHC), using changes in simultaneously recorded wearable seismocardiogram (SCG) signals captured with a small wearable patch. A total of 20 patients with HF (20% women, median age 55 (interquartile range (IQR), 44-64) years, ejection fraction 24 (IQR, 16-43)) were fitted with a wearable sensing patch and underwent RHC with vasodilator challenge. We divided the dataset randomly into a training-testing set (n = 15) and a separate validation set (n = 5). We developed globalized (population) regression models to estimate changes in PAM and PCWP from the changes in simultaneously recorded SCG. The regression model estimated both pressures with good accuracies: root-mean-square-error (RMSE) of 2.5 mmHg and R² of 0.83 for estimating changes in PAM, and RMSE of 1.9 mmHg and R² of 0.93 for estimating changes in PCWP for the training-testing set, and RMSE of 2.7 mmHg and R² of 0.81 for estimating changes in PAM, and RMSE of 2.9 mmHg and R² of 0.95 for estimating changes in PCWP for the validation set respectively. Changes in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF. This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.