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Background <p>Non-invasive, continuous blood pressure monitoring technologies require additional validation beyond standard cuff-based methods. This study evaluates a non-invasive, multiparametric wearable cuffless blood pressure (BP) diagnostic monitor across all hypertension classes with diverse subjects.</p> Methods <p>A prospective, multicenter study assessed Nanowear's SimpleSense-BP performance, including induced and natural BP changes, significant BP variations (Systolic BP (SBP) ≥ ± 15 mm Hg and Diastolic BP (DBP) ≥ ± 10 mm Hg), and reference input value validity over 4 weeks.</p> Results <p>303 subjects (18–83 yrs; 50.16% Female) participated in algorithmic development and validation (Normal – 35%, Prehypertensive – 24%, Stage 1 – 24%, Stage 2 – 17%). 54 subjects were tested for induced change performance, 149 exhibited significant changes, and 91 validated reference value duration.</p> Conclusions <p>The study clinically validated a continuous, AI-based BP diagnostic monitor using non-invasive wearable data. Further testing on diverse populations and external validation are recommended. The protocol was inspired by ISO 81060–2 and IEEE 1708:2019 standards.</p> Graphical Abstract <p></p>

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Development and Internal Validation of an AI-Enabled Cuff-less, Non-invasive Continuous Blood Pressure Monitor Across All Classes of Hypertension

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月13日

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  • Francisco Lopez-Jimenez,
  • Abhishek Deshmukh,
  • John Bisognano,
  • John Boehmer,
  • Mouli Ramasamy,
  • Prashanth Shyam Kumar,
  • Suraj Kapa,
  • Venk Varadan,
  • Vijay Varadan,
  • Marat Fudim

摘要

Background

Non-invasive, continuous blood pressure monitoring technologies require additional validation beyond standard cuff-based methods. This study evaluates a non-invasive, multiparametric wearable cuffless blood pressure (BP) diagnostic monitor across all hypertension classes with diverse subjects.

Methods

A prospective, multicenter study assessed Nanowear's SimpleSense-BP performance, including induced and natural BP changes, significant BP variations (Systolic BP (SBP) ≥ ± 15 mm Hg and Diastolic BP (DBP) ≥ ± 10 mm Hg), and reference input value validity over 4 weeks.

Results

303 subjects (18–83 yrs; 50.16% Female) participated in algorithmic development and validation (Normal – 35%, Prehypertensive – 24%, Stage 1 – 24%, Stage 2 – 17%). 54 subjects were tested for induced change performance, 149 exhibited significant changes, and 91 validated reference value duration.

Conclusions

The study clinically validated a continuous, AI-based BP diagnostic monitor using non-invasive wearable data. Further testing on diverse populations and external validation are recommended. The protocol was inspired by ISO 81060–2 and IEEE 1708:2019 standards.

Graphical Abstract