UbiHeart:通过实时面部视频进行无创血压监测的新方法

Q2 Health Professions
Kazi Shafiul Alam, Sayed Mashroor Mamun, Masud Rabbani, Parama Sridevi, Sheikh Iqbal Ahamed
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引用次数: 0

摘要

监测血压(BP)是评估心血管健康状况的重要组成部分,有助于早期发现和控制高血压相关并发症。传统的血压测量方法通常需要使用侵入性或笨重的设备,从而导致不适感和依从性降低。我们提出了一种无创血压监测框架,利用基于图像的脉搏传输时间(iPTT)和心率变异性(HRV)与血压的关系,分析网络摄像头或智能手机摄像头捕捉到的面部视频。我们建立了一个数据集,其中包括使用手机前置摄像头采集的 90 组视频,以及来自标准数字血压计的血压数据,这些数据来自获得机构审查委员会(IRB)批准的 12 名个人,用于评估我们的系统。在使用心率变异表示法 RMSSD 时,收缩压(SBP)的平均绝对误差(MAE)为 10.35+/-2.5 mmHg,舒张压(DBP)的平均绝对误差(MAE)为 7.8+/-1.5 mmHg。另一方面,使用心率变异表示法 SDRR 时,SBP 的 MAE 为 8.25+/-3.5 mmHg,DBP 为 7.7+/-2.5 mmHg。最后,我们开发了一个框架,并建立了一个实时系统来监测血压,作为一个移动和基于网络的应用程序,它可以促进早期发现趋势和异常,使医疗服务提供者能够及时干预并制定个性化的治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UbiHeart: A novel approach for non-invasive blood pressure monitoring through real-time facial video

Monitoring blood pressure (BP) is an essential component of evaluating cardiovascular health, aiding in the early detection and management of hypertension-related complications. Traditional methods of BP measurement often involve invasive or cumbersome devices, leading to discomfort and reduced compliance. We propose a framework to monitor BP non-invasively, analyzing the face video captured by a webcam or smartphone camera leveraging the relationship of image-based Pulse Transit Time (iPTT) and Heart Rate Variability (HRV) with BP. We have built a dataset of 90 sets of collected videos using a mobile phone front camera and BP data from a standard digital BP monitor from 12 individuals from an approved Institutional Review Board (IRB) to evaluate our system. We have got a Mean Absolute Error (MAE) of 10.35+/2.5 mmHg for systolic BP (SBP) and 7.8+/1.5 mmHg for diastolic BP (DBP) while using the HRV representation RMSSD. On the other hand, an MAE of 8.25+/3.5 mmHg for SBP and 7.7+/2.5 mmHg for DBP while using the HRV representation SDRR. Finally, we have developed a framework and built a real-time system to monitor BP as a mobile and web-based application that can facilitate early detection of trends and anomalies, allowing healthcare providers to intervene promptly and personalize treatment plans.

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来源期刊
Smart Health
Smart Health Computer Science-Computer Science Applications
CiteScore
6.50
自引率
0.00%
发文量
81
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