Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

Philip Westphal; Hongxing Luo; Mehrdad Shahmohammadi; Luuk I B Heckman; Marion Kuiper; Frits W Prinzen; Tammo Delhaas; Richard N Cornelussen

doi:10.3389/fcvm.2022.763048

Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

Front Cardiovasc Med. 2022 May 25:9:763048. doi: 10.3389/fcvm.2022.763048. eCollection 2022.

Authors

Philip Westphal^{1

2}, Hongxing Luo¹, Mehrdad Shahmohammadi³, Luuk I B Heckman¹, Marion Kuiper¹, Frits W Prinzen¹, Tammo Delhaas³, Richard N Cornelussen^{1

2}

Affiliations

¹ Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, Netherlands.
² Bakken Research Center, Medtronic, plc, Maastricht, Netherlands.
³ Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, Netherlands.

Abstract

Objective: A method to estimate absolute left ventricular (LV) pressure and its maximum rate of rise (LV dP/dtmax) from epicardial accelerometer data and machine learning is proposed.

Methods: Five acute experiments were performed on pigs. Custom-made accelerometers were sutured epicardially onto the right ventricle, LV, and right atrium. Different pacing configurations and contractility modulations, using isoflurane and dobutamine infusions, were performed to create a wide variety of hemodynamic conditions. Automated beat-by-beat analysis was performed on the acceleration signals to evaluate amplitude, time, and energy-based features. For each sensing location, bootstrap aggregated classification tree ensembles were trained to estimate absolute maximum LV pressure (LVPmax) and LV dP/dtmax using amplitude, time, and energy-based features. After extraction of acceleration and pressure-based features, location specific, bootstrap aggregated classification ensembles were trained to estimate absolute values of LVPmax and its maximum rate of rise (LV dP/dtmax) from acceleration data.

Results: With a dataset of over 6,000 beats, the algorithm narrowed the selection of 17 predefined features to the most suitable 3 for each sensor location. Validation tests showed the minimal estimation accuracies to be 93% and 86% for LVPmax at estimation intervals of 20 and 10 mmHg, respectively. Models estimating LV dP/dtmax achieved an accuracy of minimal 93 and 87% at estimation intervals of 100 and 200 mmHg/s, respectively. Accuracies were similar for all sensor locations used.

Conclusion: Under pre-clinical conditions, the developed estimation method, employing epicardial accelerometers in conjunction with machine learning, can reliably estimate absolute LV pressure and its first derivative.

Keywords: animal; artificial intelligence; cardiac resynchronization therapy; epicardial acceleration; heart sound; hemodynamics; machine learning.