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使用数字血压计评估基于图像光电容积脉搏波描记法的心率和血压估计。

Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter.

作者信息

Trirongjitmoah Suchin, Promking Arphorn, Kaewdang Khanittha, Phansiri Nisarut, Treeprapin Kriengsak

机构信息

Department of Electrical and Electronics Engineering, Faculty of Engineering, Ubon Ratchathani University, 85 Sathonlamark, Warinchamrab, Ubon Ratchathani 34190, Thailand.

Department of Mathematics, Statistics and Computers, Faculty of Science, Ubon Ratchathani University, 85 Sathonlamark, Warinchamrab, Ubon Ratchathani 34190, Thailand.

出版信息

Heliyon. 2024 Feb 24;10(5):e27113. doi: 10.1016/j.heliyon.2024.e27113. eCollection 2024 Mar 15.

DOI:10.1016/j.heliyon.2024.e27113
PMID:38439889
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10909774/
Abstract

This study presents a non-contact approach to measuring heart rate and blood pressure using an image photoplethysmography (iPPG) signal, and compares the results to those from an oscillometric blood pressure meter. Facial videos of 100 subjects were recorded via a webcam under ambient lighting conditions to extract iPPG signals. The results revealed a strong correlation between the heart rate derived from iPPG and that obtained from an oscillometric blood pressure meter. In addition, a continuous wavelet transform images with a 6-s duration were used as input for a custom convolutional neural network model, providing the most accurate blood pressure estimation. The proposed method received a grade A for diastolic and grade B for systolic blood pressure based on the British Hypertension Society's criteria. It also met the standards set by the Association for the Advancement of Medical Instrumentation. This non-contact framework shows promising potential for efficient screening purposes.

摘要

本研究提出了一种使用图像光电容积脉搏波描记法（iPPG）信号测量心率和血压的非接触式方法，并将结果与示波血压计的结果进行比较。在环境光照条件下，通过网络摄像头记录了100名受试者的面部视频，以提取iPPG信号。结果显示，从iPPG得出的心率与从示波血压计获得的心率之间存在很强的相关性。此外，将持续6秒的连续小波变换图像用作定制卷积神经网络模型的输入，可提供最准确的血压估计。根据英国高血压学会的标准，该方法的舒张压评级为A，收缩压评级为B。它还符合医疗仪器促进协会设定的标准。这种非接触式框架在高效筛查方面显示出有前景的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/da87238cac4b/gr9.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/da87238cac4b/gr9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/27f32288a36e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/1292883f23ed/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/0b377d0f862e/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/ef9c139b11e4/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/049b344836bd/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/3b104f6176ac/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/16b8b70674ae/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/98699cf19d77/gr8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fed9/10909774/da87238cac4b/gr9.jpg

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本文引用的文献

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Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals.基于 ECG 和 PPG 信号的混合 CNN-SVR 血压估计模型
Sensors (Basel). 2023 Jan 22;23(3):1259. doi: 10.3390/s23031259.
3
Wearable blood pressure measurement devices and new approaches in hypertension management: the digital era.可穿戴血压测量设备与高血压管理新方法：数字时代。
J Hum Hypertens. 2022 Nov;36(11):945-951. doi: 10.1038/s41371-022-00675-z. Epub 2022 Mar 23.
4
Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study.深入探讨在医学成像中使用ImageNet预训练模型与轻量级卷积神经网络的适用性：一项实验研究。
PeerJ Comput Sci. 2021 Sep 28;7:e715. doi: 10.7717/peerj-cs.715. eCollection 2021.
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iPPG 2 cPPG: Reconstructing contact from imaging photoplethysmographic signals using U-Net architectures.iPPG 2 cPPG：使用 U-Net 架构从成像光体积描记信号中重建接触。
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