Methods for Continuous Blood Pressure Estimation Using Temporal Convolutional Neural Networks and Ensemble Empirical Mode Decomposition
Electronics
; 11(9):1378, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1837990
ABSTRACT
Arterial blood pressure is not only an important index that must be measured in routine physical examination but also a key monitoring parameter of the cardiovascular system in cardiac surgery, drug testing, and intensive care. To improve the measurement accuracy of continuous blood pressure, this paper uses photoplethysmography (PPG) signals to estimate diastolic blood pressure and systolic blood pressure based on ensemble empirical mode decomposition (EEMD) and temporal convolutional network (TCN). In this method, the clean PPG signal is decomposed by EEMD to obtain n-order intrinsic mode functions (IMF), and then the IMF and the original PPG are input into the constructed TCN neural network model, and the results are output. The results show that TCN has better performance than CNN, CNN-LSTM, and CNN-GRU. Using the data added with IMF, the results of the above neural network model are better than those of the model with only PPG as input, in which the systolic blood pressure (SBP) and diastolic blood pressure (DBP) results of EEMD-TCN are −1.55 ± 9.92 mmHg and 0.41 ± 4.86 mmHg. According to the estimation results, DBP meets the requirements of the AAMI standard, BHS evaluates it as Grade A, SD of SBP is close to the standard AAMI, and BHS evaluates it as Grade B.
Electronics; blood pressure; PPG; temporal convolutional networks; ensemble empirical mode decomposition; Physiology; Machine learning; Accuracy; Quality; Deep learning; Regression analysis; Artificial neural networks; Neural networks; Support vector machines; Medical research; Cardiovascular system; Algorithms; Time series; Coronaviruses; COVID-19; Disease transmission
Search on Google
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
Electronics
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS