Roy Dibyendu, Mazumder Oishee, Chakravarty Kingshuk, Sinha Aniruddha, Ghose Avik, Pal Arpan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:918-922. doi: 10.1109/EMBC44109.2020.9175352.
Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.
合成准确的个性化光电容积脉搏波图（PPG）信号对于解释、分析和预测心血管疾病进展至关重要。像生成对抗网络（GANs）这样的生成模型可用于信号合成，然而，它们难以映射到潜在的病理生理状况。因此，我们提出了一种PPG合成策略，该策略是使用通过血流动力学原理建模的心血管系统设计的。建模架构由一个双腔心脏以及体肺血液循环和一个控制动脉血压的压力反射自动调节机制组成。综合PPG信号由心脏压力 - 血流动力学合成。为了根据测量的PPG数据调整建模的心脏参数，采用了一种新颖的特征提取策略以及粒子群优化启发式算法。我们的结果表明，合成的PPG准确地跟随了真实（GT）信号的形态变化，由于动脉阻塞导致的冠状动脉疾病（CAD），均方根误差（RMSE）为0.003。
