We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography.
- Authors
Sadrawi, Muammar; Lin, Yin-Tsong; Lin, Chien-Hung; Mathunjwa, Bhekumuzi; Fan, Shou-Zen; Abbod, Maysam F.; Shieh, Jiann-Shing
- 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.
- Subjects
BLOOD pressure; SYSTOLIC blood pressure; PHOTOPLETHYSMOGRAPHY
- Publication
Sensors (14248220), 2020, Vol 20, Issue 14, p3829
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s20143829