Please use this identifier to cite or link to this item:
|Title:||Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography|
|Keywords:||Photoplethysmography;Continuous arterial blood pressure;Systolic blood pressure;Diastolic blood pressure;Deep convolutional autoencoder;Genetic algorithm|
|Citation:||Sadrawi, M.; Lin, Y.-T.; Lin, C.-H.; Mathunjwa, B.; Fan, S.-Z.; Abbod, M.F.; Shieh, J.-S. Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography. Sensors 2020, 20, 3829.|
|Abstract:||Hypertension affects a huge number of people around the world. It also has a great contribution to cardiovascular- and renal-related diseases. This study investigates the ability of a deep convolutional autoencoder (DCAE) to generate continuous arterial blood pressure (ABP) by only utilizing photoplethysmography (PPG). A total of 18 patients are utilized. LeNet-5- and U-Net-based DCAEs, respectively abbreviated LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the gold standard. Moreover, in order to investigate the data generalization, the cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the systolic blood pressure (SBP) estimation. Meanwhile, the LDCAE gives a slightly better result for the diastolic blood pressure (DBP) prediction. Finally, the genetic algorithm-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. In conclusion, this study exhibits that systolic blood pressure (SBP) and diastolic blood pressure (DBP) can also be accurately achieved by only utilizing a single PPG signal.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.