Key result
The deep-learning-based multi-channel PPG system with an attention mechanism estimated systolic blood pressure with a mean error of 0.43 ± 9.35 mmHg and diastolic blood pressure with a mean error of 0.21 ± 7.72 mmHg.
Why the study?
Cuffless blood pressure measurement using the oscillometric method is prone to errors caused by finger position.
Does a deep-learning-based multi-channel PPG and finger pressure system accurately estimate blood pressure compared to standard auscultation?
Observational (n=476)
Yes
Does a deep-learning-based multi-channel PPG and finger pressure system accurately estimate blood pressure compared to standard auscultation?
Mean Difference: 0.43
A novel deep-learning-based multi-channel PPG system with an attention mechanism provides cuffless blood pressure estimation that approaches AAMI standards.
Recently, several studies have proposed methods for measuring cuffless blood pressure (BP) using finger photoplethysmogram (PPG) signals. This study presents a new BP estimation system that measures PPG signals under progressive finger pressure, making the system relatively robust to errors caused by finger position when using the cuffless oscillometric method. To reduce errors caused by finger position, we developed a sensor that can simultaneously measure multi-channel PPG and force signals in a wide field of view (FOV). We propose a deep-learning-based algorithm that can learn to focus on the optimal PPG channel from multi channel PPG using an attention mechanism. The errors (ME±STD) of the proposed multi channel system were 0.43±9.35 mmHg and 0.21±7.72 mmHg for SBP and DBP, respectively. Through extensive experiments, we found a significant performance difference depending on the location of the PPG measurement in the BP estimation system using finger pressure.
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Kyung et al. (2022) conducted an observational in Blood pressure (n=476). Deep-learning-based multi-channel PPG and finger pressure system vs. Auscultation method was evaluated on Mean error (ME) and standard deviation (STD) for systolic blood pressure (SBP) (ME 0.43 ± 9.35 mmHg). The deep-learning-based multi-channel PPG system with an attention mechanism estimated systolic blood pressure with a mean error of 0.43 ± 9.35 mmHg and diastolic blood pressure with a mean error of 0.21 ± 7.72 mmHg.
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