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

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 photopl...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 20; číslo 14; s. 3829
Hlavní autoři: Sadrawi, Muammar, Lin, Yin-Tsong, Lin, Chien-Hung, Mathunjwa, Bhekumuzi, Fan, Shou-Zen, Abbod, Maysam F., Shieh, Jiann-Shing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 09.07.2020
MDPI
Témata:
Algorithms
Arterial Pressure
Blood Pressure
Blood Pressure Determination
Cardiac arrhythmia
continuous arterial blood pressure
Datasets
deep convolutional autoencoder
Deep learning
diastolic blood pressure
Electrocardiography
Gas turbine engines
genetic algorithm
Humans
Hypertension
Hypertension - diagnosis
Neural networks
Patients
Photoplethysmography
systolic blood pressure
Vital signs
United States > US
genetic algorithm
deep convolutional autoencoder
diastolic blood pressure
photoplethysmography
continuous arterial blood pressure
systolic blood pressure
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20143829