A multi-stage deep learning blood pressure prediction model based on IPPG signals conformed to BHS and AAMI international standards, improving accuracy over other methods.
Does a multi-stage deep learning model based on non-contact IPPG signals improve blood pressure measurement accuracy compared to other methods?
A novel non-contact IPPG signal acquisition system and deep learning model can accurately estimate blood pressure in accordance with BHS and AAMI standards.
In this paper, a multi-stage deep learning blood pressure prediction model based on imaging photoplethysmography (IPPG) signals is proposed to achieve accurate and convenient monitoring of human blood pressure. A camera-based non-contact human IPPG signal acquisition system is designed. The system can perform experimental acquisition under ambient light, effectively reducing the cost of non-contact pulse wave signal acquisition while simplifying the operation process. The first open-source dataset IPPG-BP for IPPG signal and blood pressure data is constructed by this system, and a multi-stage blood pressure estimation model combining a convolutional neural network and bidirectional gated recurrent neural network is designed. The results of the model conform to both BHS and AAMI international standards. Compared with other blood pressure estimation methods, the multi-stage model automatically extracts features through a deep learning network and combines different morphological features of diastolic and systolic waveforms, which reduces the workload while improving accuracy.
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Sensors
Zhejiang Normal University
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Cheng et al. (Tue,) conducted a other in Blood pressure measurement. Multi-stage deep learning blood pressure prediction model based on IPPG signals vs. Other blood pressure estimation methods was evaluated on Conformance to BHS and AAMI international standards. A multi-stage deep learning blood pressure prediction model based on IPPG signals conformed to BHS and AAMI international standards, improving accuracy over other methods.