Crisp-BP:基于ppg的连续手腕血压测量

Yetong Cao, Huijie Chen, Fan Li, Yu Wang
{"title":"Crisp-BP:基于ppg的连续手腕血压测量","authors":"Yetong Cao, Huijie Chen, Fan Li, Yu Wang","doi":"10.1145/3447993.3483241","DOIUrl":null,"url":null,"abstract":"Arterial blood pressure (ABP) monitoring using wearables has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. However, existing schemes mainly monitor ABP at discrete time intervals, involve some form of user effort, have insufficient accuracy, and require collecting sufficient training data for model development. To tackle these problems, we propose Crisp-BP, a novel ABP monitoring system leveraging the PPG sensor available in commercial wrist-worn devices (e.g., smartwatches or fitness trackers). It enables continuous, accurate, user-independent ABP monitoring and requires no behavior changes during collecting PPG data. The basic idea is to illuminate a skin/tissue, measure the light absorption, and characterize ABP-related blood volume change in the artery. To obtain accurate measurements and relieve the pain of training data collection, we use an arterial pulse extraction method that removes interference caused by capillary pulses. Moreover, we design a contact pressure estimation method to combat the deficiency of PPG waveform being sensitive to the contact pressure between the sensor and the skin. In addition, we leverage the great power of Bidirectional Long Short Term Memory and design a hybrid neural network model to enable user-independent ABP monitoring, so that users do not have to provide training data for model development. Furthermore, we propose a transfer learning method that first extracts general knowledge from online PPG data, then use it to improve the learning of a new model on our target problem. Extensive experiments with 35 participants demonstrate that Crisp-BP obtains the average estimation error of 0.86 mmHg and 1.67 mmHg and the standard deviation error of 6.55 mmHg and 7.31 mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range regulated by the FDA's AAMI protocol, which allows average errors of up to 5 mmHg and a standard deviation of up to 8 mmHg. Our results demonstrate that Crisp-BP is promising for improving the diagnosis and control of hypertension as it provides continuousness, comfort, convenience, and accuracy.","PeriodicalId":177431,"journal":{"name":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Crisp-BP: continuous wrist PPG-based blood pressure measurement\",\"authors\":\"Yetong Cao, Huijie Chen, Fan Li, Yu Wang\",\"doi\":\"10.1145/3447993.3483241\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Arterial blood pressure (ABP) monitoring using wearables has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. However, existing schemes mainly monitor ABP at discrete time intervals, involve some form of user effort, have insufficient accuracy, and require collecting sufficient training data for model development. To tackle these problems, we propose Crisp-BP, a novel ABP monitoring system leveraging the PPG sensor available in commercial wrist-worn devices (e.g., smartwatches or fitness trackers). It enables continuous, accurate, user-independent ABP monitoring and requires no behavior changes during collecting PPG data. The basic idea is to illuminate a skin/tissue, measure the light absorption, and characterize ABP-related blood volume change in the artery. To obtain accurate measurements and relieve the pain of training data collection, we use an arterial pulse extraction method that removes interference caused by capillary pulses. Moreover, we design a contact pressure estimation method to combat the deficiency of PPG waveform being sensitive to the contact pressure between the sensor and the skin. In addition, we leverage the great power of Bidirectional Long Short Term Memory and design a hybrid neural network model to enable user-independent ABP monitoring, so that users do not have to provide training data for model development. Furthermore, we propose a transfer learning method that first extracts general knowledge from online PPG data, then use it to improve the learning of a new model on our target problem. Extensive experiments with 35 participants demonstrate that Crisp-BP obtains the average estimation error of 0.86 mmHg and 1.67 mmHg and the standard deviation error of 6.55 mmHg and 7.31 mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range regulated by the FDA's AAMI protocol, which allows average errors of up to 5 mmHg and a standard deviation of up to 8 mmHg. Our results demonstrate that Crisp-BP is promising for improving the diagnosis and control of hypertension as it provides continuousness, comfort, convenience, and accuracy.\",\"PeriodicalId\":177431,\"journal\":{\"name\":\"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3447993.3483241\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3447993.3483241","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

使用可穿戴设备监测动脉血压(ABP)已经成为一种很有前途的方法,使用户能够通过自我监测来有效诊断和控制高血压。然而,现有的方案主要以离散的时间间隔监测ABP,涉及某种形式的用户工作,准确性不足,并且需要为模型开发收集足够的训练数据。为了解决这些问题,我们提出了Crisp-BP,一种利用商业腕戴设备(例如智能手表或健身追踪器)中可用的PPG传感器的新型ABP监测系统。它可以实现连续、准确、独立于用户的ABP监测,并且在收集PPG数据期间不需要改变行为。基本思想是照亮皮肤/组织,测量光吸收,并表征动脉中与abp相关的血容量变化。为了获得准确的测量结果,减轻训练数据收集的痛苦,我们使用了动脉脉冲提取方法,消除了毛细血管脉冲造成的干扰。此外,我们设计了一种接触压力估计方法,以克服PPG波形对传感器与皮肤之间的接触压力敏感的不足。此外,我们利用双向长短期记忆的强大功能,设计了一种混合神经网络模型,实现与用户无关的ABP监测,使用户无需为模型开发提供训练数据。此外,我们提出了一种迁移学习方法,该方法首先从在线PPG数据中提取一般知识,然后使用它来改进新模型对目标问题的学习。35名参与者的广泛实验表明,Crisp-BP对舒张压和收缩压的平均估计误差分别为0.86和1.67 mmHg,标准差误差分别为6.55和7.31 mmHg。这些误差在FDA AAMI协议规定的可接受范围内,该协议允许平均误差高达5毫米汞柱,标准偏差高达8毫米汞柱。我们的研究结果表明,Crisp-BP具有连续性、舒适性、便捷性和准确性,有望改善高血压的诊断和控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crisp-BP: continuous wrist PPG-based blood pressure measurement
Arterial blood pressure (ABP) monitoring using wearables has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. However, existing schemes mainly monitor ABP at discrete time intervals, involve some form of user effort, have insufficient accuracy, and require collecting sufficient training data for model development. To tackle these problems, we propose Crisp-BP, a novel ABP monitoring system leveraging the PPG sensor available in commercial wrist-worn devices (e.g., smartwatches or fitness trackers). It enables continuous, accurate, user-independent ABP monitoring and requires no behavior changes during collecting PPG data. The basic idea is to illuminate a skin/tissue, measure the light absorption, and characterize ABP-related blood volume change in the artery. To obtain accurate measurements and relieve the pain of training data collection, we use an arterial pulse extraction method that removes interference caused by capillary pulses. Moreover, we design a contact pressure estimation method to combat the deficiency of PPG waveform being sensitive to the contact pressure between the sensor and the skin. In addition, we leverage the great power of Bidirectional Long Short Term Memory and design a hybrid neural network model to enable user-independent ABP monitoring, so that users do not have to provide training data for model development. Furthermore, we propose a transfer learning method that first extracts general knowledge from online PPG data, then use it to improve the learning of a new model on our target problem. Extensive experiments with 35 participants demonstrate that Crisp-BP obtains the average estimation error of 0.86 mmHg and 1.67 mmHg and the standard deviation error of 6.55 mmHg and 7.31 mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range regulated by the FDA's AAMI protocol, which allows average errors of up to 5 mmHg and a standard deviation of up to 8 mmHg. Our results demonstrate that Crisp-BP is promising for improving the diagnosis and control of hypertension as it provides continuousness, comfort, convenience, and accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信