Scholarlink

<p>We investigated cuffless blood pressure estimation from facial videos using remote photoplethysmography (rPPG) with an RGB–NIR camera. We obtained data from 31 participants with a chin rest and five without a chin rest, and rPPG signals from the nasal and cheek regions were processed to extract waveform features. Blood pressure was estimated using a light gradient boosting machine (Light GBM) and evaluated using leave-one-subject-out (LOSO) and within-subject sixfold cross-validation. Compared with prior support vector regression, Light GBM showed improved accuracy (LOSO: systolic blood pressure (SBP), 11.9&#xa0;mmHg; diastolic blood pressure (DBP), 6.7&#xa0;mmHg; sixfold: SBP, 8.9&#xa0;mmHg; DBP, 5.2&#xa0;mmHg). According to British Hypertension Society (BHS) standards, DBP estimation achieved Grade A, whereas SBP was classified as Grade D, albeit close to Grade C. Shorter acquisition durations of 5 and 10&#xa0;s, compared with the conventional 60&#xa0;s recording, as well as chin rest-free measurements, demonstrated that cuffless blood pressure estimation was feasible in the absence of large facial motions that disrupted rPPG signals. These results demonstrate the feasibility of cuffless blood pressure estimation from RGB–NIR facial videos using Light GBM, particularly for DBP estimation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Towards Practical Cuffless Blood Pressure Estimation Using RGB–NIR Camera and Light Gradient Boosting Machine

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

查看全文
  • Masaya Kinefuchi,
  • Sae Kawasaki,
  • Motoya Sato,
  • Shoji Kawahito,
  • Masato Takahashi,
  • Norimichi Tsumura

摘要

We investigated cuffless blood pressure estimation from facial videos using remote photoplethysmography (rPPG) with an RGB–NIR camera. We obtained data from 31 participants with a chin rest and five without a chin rest, and rPPG signals from the nasal and cheek regions were processed to extract waveform features. Blood pressure was estimated using a light gradient boosting machine (Light GBM) and evaluated using leave-one-subject-out (LOSO) and within-subject sixfold cross-validation. Compared with prior support vector regression, Light GBM showed improved accuracy (LOSO: systolic blood pressure (SBP), 11.9 mmHg; diastolic blood pressure (DBP), 6.7 mmHg; sixfold: SBP, 8.9 mmHg; DBP, 5.2 mmHg). According to British Hypertension Society (BHS) standards, DBP estimation achieved Grade A, whereas SBP was classified as Grade D, albeit close to Grade C. Shorter acquisition durations of 5 and 10 s, compared with the conventional 60 s recording, as well as chin rest-free measurements, demonstrated that cuffless blood pressure estimation was feasible in the absence of large facial motions that disrupted rPPG signals. These results demonstrate the feasibility of cuffless blood pressure estimation from RGB–NIR facial videos using Light GBM, particularly for DBP estimation.