Westphal Philip, Luo Hongxing, Shahmohammadi Mehrdad, Heckman Luuk I B, Kuiper Marion, Prinzen Frits W, Delhaas Tammo, Cornelussen Richard N
Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, Netherlands.
Bakken Research Center, Medtronic, plc, Maastricht, Netherlands.
Front Cardiovasc Med. 2022 May 25;9:763048. doi: 10.3389/fcvm.2022.763048. eCollection 2022.
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.
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.
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.
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.
提出一种从心外膜加速度计数据和机器学习估算绝对左心室（LV）压力及其最大上升速率（LV dP/dtmax）的方法。
对猪进行了五项急性实验。将定制的加速度计心外膜缝合到右心室、左心室和右心房上。使用异氟烷和多巴酚丁胺输注进行不同的起搏配置和收缩性调节，以创造多种血流动力学条件。对加速度信号进行逐搏自动分析，以评估基于幅度、时间和能量的特征。对于每个传感位置，使用基于幅度、时间和能量的特征训练自助聚合分类树集成，以估算绝对最大左心室压力（LVPmax）和LV dP/dtmax。在提取基于加速度和压力的特征后，训练特定位置的自助聚合分类集成，以从加速度数据估算LVPmax及其最大上升速率（LV dP/dtmax）的绝对值。
对于一个超过6000次搏动的数据集，该算法将17个预定义特征的选择范围缩小到每个传感器位置最合适的3个特征。验证测试表明，在20和10 mmHg的估计间隔下，LVPmax的最小估计准确率分别为93%和86%。估计LV dP/dtmax的模型在100和200 mmHg/s的估计间隔下，准确率分别至少达到93%和87%。所使用的所有传感器位置的准确率相似。
在临床前条件下，所开发的采用心外膜加速度计结合机器学习的估计方法能够可靠地估算绝对左心室压力及其一阶导数。
