A portable blood pressure prediction system using a wrist PPG sensor and artificial neural networks estimated SBP with an error of -0.07 ± 4.47 mmHg and DBP with an error of 0.00 ± 3.61 mmHg.
Does a portable blood pressure prediction system based on wrist PPG and artificial neural networks accurately estimate continuous blood pressure?
A novel wrist PPG-based artificial neural network model provides accurate continuous blood pressure estimation, meeting European Society of Hypertension standards.
Effect estimate: SBP error -0.07 ± 4.47 mmHg; DBP error 0.00 ± 3.61 mmHg
The cuff-less blood pressure (BP) monitoring method based on photoplethysmo- gram (PPG) makes it possible for long-term BP monitoring to prevent and treat cardiovascular and cerebrovascular events. In this paper, a portable BP prediction system based on feature combination and artificial neural network (ANN) is implemented. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm was used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features were extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm was used for regression. Thirty-three subjects distributed in the three BP groups were recruited. The proposed method passed the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of -0.07 ± 4.47 mmHg for SBP and 0.00 ± 3.61 mmHg for DBP. Moreover, the model tracked the BP of two subjects for half a month, laying the foundation work for daily BP monitoring. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.
Yao et al. (Wed,) conducted a other in Blood pressure monitoring (n=33). Portable BP prediction system based on wrist PPG sensor and ANN vs. Standard BP measurement (ESP-IP2 protocol) was evaluated on Estimation error for SBP and DBP (SBP error -0.07 ± 4.47 mmHg; DBP error 0.00 ± 3.61 mmHg). A portable blood pressure prediction system using a wrist PPG sensor and artificial neural networks estimated SBP with an error of -0.07 ± 4.47 mmHg and DBP with an error of 0.00 ± 3.61 mmHg.