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

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: Changes in wearable SCG signals may be used to track acute changes in intracardiac hemodynamics in patients with HF.

Significance: This method holds promise in tracking longitudinal changes in hemodynamic congestion in hemodynamically-guided remote home monitoring and treatment for patients with HF.

Fig. 1.

(a) Experimental setup with a wearable patch placed on a subject undergoing right heart catheterization (RHC) procedure, with axes (on the upper-right) showing the axes of the seismocardiogram (SCG) signal. (b) Front (left) and side (right) view of a wearable patch placed on a representative subject. (c) Representative cardiogenic signals: electrocardiogram (ECG), triaxial SCG (head-to-foot (HtoF), lateral (Lat), and dorsoventral (DV)), and RHC pulmonary artery pressure (PAP) signal. SCG is a mechanical signal that has been associated with cardiac muscle contraction, cardiac valve movement, and movement of the blood from the left ventricle towards the aorta.

Fig. 2.

Overview of the method: (a) Wearable ECG and SCG (only showing one axis of the signal for simplicity) signals were synchronized with the right heart catheterization pressure (RHCP) signal. 20s long signals from both baseline (BL) and during vasodilator infusion (VI) were extracted when the catheter was recording pulmonary artery (PA) pressure and in pulmonary capillary wedge (PCW) pressure signals. (b) The R-peaks of the ECG signal were detected and later used to segment the corresponding SCG signals into individual heartbeats. Outlier removal and noise reduction steps were performed on the SCG heartbeats, and features were extracted to be used in the regression algorithm to estimate the changes in the RHC mean pressure (MP) values (e.g., changes in pulmonary artery mean pressure (ΔPAM), and changes in pulmonary capillary wedge mean pressure (ΔPCWP)). The MP_BL and MP_VI values were extracted from the RHC Mac-Lab computer and used to calculate the target variable (ΔPAM and ΔPCWP). (c) Details on the wearable signal processing: First, the R-peaks of the ECG signals were detected, and the SCG signals were segmented into individual heartbeats. Second, SCG_BL and SCG_VI heartbeats were passed through an outlier removal algorithm (using principal component analysis [PCA] and Gaussian mixture model (GMM)) and were ensemble-averaged to have two average SCG heartbeats per axis (one for BL and one for VI). Third, dynamic time warping (DTW) distances were calculated between the BL and VI heartbeats per axes and used as features (f) in the regression algorithm.

Fig. 3.

Changes in (a) pulmonary artery pressure (PAP) and (b) pulmonary capillary wedge pressure (PCWP), with the infusion of vasodilator for a representative subject, with brown arrows showing the changes in the respective signals. Time “0” indicates the location of the corresponding ECG R-peak.

Fig. 4.

Changes in SCG in the dorso-ventral direction (SCG_DV) with the infusion of vasodilator for a representative subject, with brown arrows showing the changes in the respective signals. Time “0” indicates the location of the corresponding ECG R-peak.

Fig. 5.

Correlation analysis of the target variable (a) ΔPAM and (b) ΔPCWP with different DTW distances of corresponding SCG signals for the training-testing set, with the colorbar showing the R² values and the red dotted line indicating the division between ventricular diastole and systole (i.e., R-peak of corresponding ECG). Total Diastole (−500ms : R-peak), early diastole (−500ms : −200 ms), late diastole (−200ms : R-peak), total systole (R-peak : 500ms), early systole (25ms : 150ms), and late systole (200ms : 500ms).

Fig. 6.

Estimation results for the training-testing set: (a) Correlation analysis for ΔPAM predicted vs. ΔPAM actual, (b) Bland-Altman analysis for ΔPAM predicted and ΔPAM actual, (c) correlation analysis for ΔPCWP predicted vs. ΔPCWP actual, and (d) Bland-Altman analysis for ΔPCWP predicted and ΔPCWP actual. In the Bland-Altman plots, the black line indicates the mean, while the blue dashed lines indicate mean ± 1.96 × standard deviation (SD).

Fig. 7.

Estimation results for the validation set: (a) Correlation analysis for ΔPAM predicted vs. ΔPAM actual, (b) Bland-Altman analysis for ΔPAM predicted and ΔPAM actual, (c) correlation analysis for ΔPCWP predicted vs. ΔPCWP actual, and (d) Bland-Altman analysis for ΔPCWP predicted and ΔPCWP actual. In the Bland-Altman plots, the black line indicates the mean, while the blue dashed lines indicate mean ± 1.96 × standard deviation (SD).

Fig. 8.

Relative feature importance ranking (i.e., relative weights) of the features in the regression algorithm for (a) ΔPAM and (b) ΔPCWP on the training-testing set. Dias: Total Diastole, ED: Early Diastole, LD: Late Diastole, Sys: Systole, ES: Early Systole, and LS: Late Systole. Time-length for the segments is explained in the Fig. 5.