Why the study?
Most photoplethysmography-based BP estimation methods are susceptible to noise and only provide discrete systolic and diastolic blood pressure predictions rather than continuous waveforms.
Does an LSTM-based deep learning model accurately estimate continuous arterial blood pressure waveforms from raw photoplethysmography signals?
Comparison
Raw PPG-to-ABP signal-to-signal translation deep learning model
Design
Algorithm development and validation study
Authors
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Offers preliminary support for PPG-based BP waveform estimation; leaves open prospective validation before clinical use.
Does an LSTM-based deep learning model accurately estimate continuous arterial blood pressure waveforms from raw photoplethysmography signals?
An LSTM-based deep learning model can accurately estimate continuous blood pressure waveforms from raw PPG signals, meeting established clinical validation standards.
Harfiya et al. (2021) studied this question.
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