Methods: A method of the pulsation for a pVAD is proposed (AP-pVAD Model). AP-pVAD Model consists of two parts: NPQ Model and LSTM-Transformer Model. (1)The NPQ Model determines the mathematical relationship between motor speed, pressure, and flow rate for the pVAD. (2)The Attention module of Transformer neural network is integrated into the LSTM neural network to form the new LSTM-Transformer Model to predict the pulsation time characteristic points for adjusting the motor speed of the pVAD. Results: The AP-pVAD Model is validated in three hydraulic experiments and an animal experiment. (1)The pressure provided by pVAD calculated with the NPQ Model has a maximum error of only 2.15 mmHg compared to the expected values. (2)The pulsation time characteristic points predicted by the LSTM-Transformer Model shows a maximum prediction error of 1.78ms, which is significantly lower than other methods. (3)The in-vivo test of pVAD in animal experiment has significant improvements in aortic pressure. Animals survive for over 27 hours after the initiation of pVAD operation. Conclusion: (1)For a given pVAD, motor speed has a linear relationship with pressure and a quadratic relationship with flow. (2)Deep learning can be used to predict pulsation characteristic time points, with the LSTM-Transformer Model demonstrating minimal prediction error and better robust performance under conditions of limited dataset sizes, elevated noise levels, and diverse hyperparameter combinations, demonstrating its feasibility and effectiveness.

翻译：方法：本文提出了一种用于pVAD搏动控制的方法（AP-pVAD模型）。AP-pVAD模型由两部分组成：NPQ模型和LSTM-Transformer模型。(1) NPQ模型确定了pVAD电机转速、压力与流量之间的数学关系。(2) 将Transformer神经网络的注意力模块集成到LSTM神经网络中，形成新的LSTM-Transformer模型，用于预测调节pVAD电机转速所需的搏动时间特征点。结果：AP-pVAD模型在三组液压实验和一组动物实验中得到了验证。(1) 使用NPQ模型计算的pVAD提供的压力与预期值相比，最大误差仅为2.15 mmHg。(2) LSTM-Transformer模型预测的搏动时间特征点最大预测误差为1.78ms，显著低于其他方法。(3) 动物实验中pVAD的体内测试显示主动脉压力有显著改善。在pVAD开始运行后，动物存活时间超过27小时。结论：(1) 对于给定的pVAD，电机转速与压力呈线性关系，与流量呈二次关系。(2) 深度学习可用于预测搏动特征时间点，其中LSTM-Transformer模型在数据集规模有限、噪声水平较高以及超参数组合多样的条件下，表现出最小的预测误差和更好的鲁棒性能，证明了其可行性和有效性。