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<p>In this study, we propose a noninvasive blood pressure estimation method using spatial remote photoplethysmography (rPPG). Multiple regions of interest were set to cover the entire face in the captured RGB facial video, and rPPG signals were extracted from each region. The pulse wave contour, pulse beat, and derivative features were obtained from the extracted data and input into a neural network with an attention feature fusion module to extract essential features while reducing bias among them. The relationship between the features and blood pressure was modeled using this network, and the blood pressure was estimated. Finally, an experiment-wise 5-fold cross-validation was performed on a dataset of six subjects. When compared to the ground truth, the correlation coefficients for the systolic and diastolic blood pressure were 0.79 and 0.75, respectively, with mean absolute errors of 6.25&#xa0;mmHg and 3.45&#xa0;mmHg, outperforming the conventional method.</p>

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Noninvasive blood pressure estimation based on spatial remote photoplethysmography using an attentional feature fusion module

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Ryosuke Imai,
  • Sae Kawasaki,
  • Rio Ishiguro,
  • Kota Toyama,
  • Masato Takahashi,
  • Norimichi Tsumura

摘要

In this study, we propose a noninvasive blood pressure estimation method using spatial remote photoplethysmography (rPPG). Multiple regions of interest were set to cover the entire face in the captured RGB facial video, and rPPG signals were extracted from each region. The pulse wave contour, pulse beat, and derivative features were obtained from the extracted data and input into a neural network with an attention feature fusion module to extract essential features while reducing bias among them. The relationship between the features and blood pressure was modeled using this network, and the blood pressure was estimated. Finally, an experiment-wise 5-fold cross-validation was performed on a dataset of six subjects. When compared to the ground truth, the correlation coefficients for the systolic and diastolic blood pressure were 0.79 and 0.75, respectively, with mean absolute errors of 6.25 mmHg and 3.45 mmHg, outperforming the conventional method.