Temporal Convolutional Neural Networks and Ensemble  Methods for Continuous Blood Pressure Estimation

Raj Jain

Abstract


Arterial blood pressure is not only an important indicator that must be evaluated in standard physical examinations, but it is also a vital cardiovascular monitoring parameter in cardiac surgery, drug testing, and intensive care. This article employs photoplethysmography (PPG) data to estimate diastolic and systolic blood pressure based on ensemble empirical mode decomposition (EEMD) and temporal convolutional network to increase the measurement accuracy of continuous blood pressure (TCN). The clean PPG signal is decomposed using EEMD to create n-order intrinsic mode functions (IMF), and the IMF and the original PPG are then fed into the built TCN neural network model, and the results are output. TCN outperforms CNN, CNN-LSTM, and CNN-GRU in terms of performance.

Keywords


ppg,eemd,tcn

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