Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification - Westphal, Philip; Luo, Hongxing; Shahmohammadi, Mehrdad; Heckman, Luuk I. B.; Kuiper, Marion; Prinzen, Frits W.; Delhaas, Tammo; Cornelussen, Richard N. (Frontiers in Cardiovascular Medicine, 2022)
Abstract (revised) Objective: Propose 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. Methods: Five acute experiments were performed in 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 in 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. 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 6000 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.