Key result
Deep neural network using PPG and ECG estimates continuous systolic BP with ~3 mmHg error.
Why the study?
Continuous BP monitoring provides invaluable information for CVD diagnosis, motivating the development of deep learning models for continuous BP estimation using PPG and ECG signals.
Does a deep RNN model with an optimal feature set of PPG and ECG signals improve the accuracy of continuous blood pressure estimation?
Does a deep RNN model with an optimal feature set of PPG and ECG signals improve the accuracy of continuous blood pressure estimation?
A deep recurrent neural network utilizing an optimal 14-feature set from PPG and ECG signals provides highly accurate, continuous beat-to-beat blood pressure estimation.
May enable cuffless continuous BP monitoring; leaves open prospective clinical validation before practice adoption.
Aim: Continuous blood pressure (BP) monitoring can provide invaluable information for cardiovascular disease (CVD) diagnosis. The purpose of this study is to develop a deep recurrent neural network (RNN) model with an optimal feature set of photoplethysmogram (PPG) and electrocardiogram (ECG) signals for continuous BP estimation. Methods: This paper presents a novel deep recurrent neural network (RNN), which consists of 2-layered bidirectional Long Short-term Memory (Bi-LSTM) and 6-layered LSTM networks. It is used to estimate BP based on the optimal feature set of PPG and ECG signals. In this work, the optimal feature set is determined using five different feature selection methods. Results: The proposed method is evaluated based on 660 subjects from the University of California Irvine (UCI) machine learning repository. The RNN model with optimal feature set achieved root mean square error (RMSE) of 3.223 and 1.781 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. It also showed mean absolute error (MAE) of 2.514 and 1.383 mmHg for SBP and DBP, respectively. Regarding the British Hypertension Society (BHS) standard, the results attained grade A for the estimation of SBP and DBP. Conclusion: The experimental results suggest that the proposed deep RNN model with an optimal feature set can improve the performance of BP prediction. Thus, it is possible to further apply our proposed method to develop a wearable device for real-time BP monitoring.
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Chen et al. (2023) studied Blood pressure estimation (n=660). Deep recurrent neural network (RNN) model using PPG and ECG signals was evaluated on Systolic and diastolic blood pressure estimation error (RMSE and MAE). A deep recurrent neural network model using PPG and ECG signals estimated continuous blood pressure with a root mean square error of 3.223 mmHg for systolic and 1.781 mmHg for diastolic BP.
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