Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Abstract

Hypertension affects huge number of people around the world. It also has a great contribution to cardiovascular and renal related diseases. This study investigates the ability deep convolutional autoencoder (DCAE) to generate the continuous arterial blood pressure (ABP) by only utilizing the photoplethysmography (PPG) to generate the continuous ABP. The total of 18 patients is utilized. LeNet-5 and U-Net based DCAEs, respectively for LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the golden standard. Moreover, in order to investigate the data generalization, leave-one-out cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the SBP estimation. Meanwhile, LDCAE gives a slightly better for the DBP prediction. Finally, the genetic algorithm (GA) based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. For conclusion, this study reveals that the SBP and DBP can also be accurately achieved by only utilizing the single PPG signal.