[1]张锡壮,梁恒源,殷世民,等.基于物联网和深度学习的血压测量系统[J].中国医学物理学杂志,2024,41(11):1383-1391.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.010]
　ZHANG Xizhuang,LIANG Hengyuan,YIN Shimin,et al.Blood pressure measurement system based on internet of things and deep learning[J].Chinese Journal of Medical Physics,2024,41(11):1383-1391.[doi:DOI:10.3969/j.issn.1005-202X.2024.11.010]
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基于物联网和深度学习的血压测量系统()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第11期
页码:
1383-1391
栏目:
医学信号处理与医学仪器
出版日期:
2024-11-26

文章信息/Info

Title:
Blood pressure measurement system based on internet of things and deep learning
文章编号:
1005-202X（2024）11-1383-09
作者:
张锡壮1; 梁恒源2; 殷世民2; 3; 陈真诚2; 4; 梁永波2; 3; 4
1.桂林电子科技大学电子工程与自动化学院， 广西 桂林 541004； 2.桂林电子科技大学生命与环境科学学院， 广西 桂林 541004；3.广西自动检测技术与仪器重点实验室， 广西 桂林 541004； 4.广西慢性病代谢病重塑与智能医学工程重点实验室， 广西 桂林 541004
Author(s):
ZHANG Xizhuang1;  LIANG Hengyuan2;  YIN Shimin2;  3;  CHEN Zhencheng2;  4;  LIANG Yongbo2;  3;  4
1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 2. School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China 3. Guangxi Key Laboratory of Automatic Testing Technology and Instrumentation, Guilin 541004, China 4. Guangxi Key Laboratory of Metabolic Disease Remodeling and Intelligent Medical Engineering for Chronic Diseases, Guilin 541004, China
关键词:
深度学习; 嵌入式; 血压; 物联网; 云计算
Keywords:
Keywords: deep learning embedded blood pressure internet of things cloud computing
分类号:
R318.6；TP368.1
DOI:
DOI:10.3969/j.issn.1005-202X.2024.11.010
文献标志码:
A
摘要:
提出一种基于物联网和深度学习的血压测量系统，用于进行连续数据采集及血压预测。为了准确预测血压，提出一种混合神经网络结构，用于处理采集的数据并进行血压预测。该模型由ResNet18、GRU和3个全连接层组成。使用设计的系统采集82例志愿者数据进行训练和测试，舒张压的平均绝对误差和标准差分别为2.16、3.09 mmHg，收缩压的平均绝对误差和标准差分别为3.15、5.14 mmHg，达到AAMI标准和BHS标准。
Abstract:
Abstract: A blood pressure measurement system based on internet of things and deep learning is proposed for continuous data acquisition and blood pressure prediction. The system adopts a hybrid neural network structure for processing the collected data and accurately predicting blood pressure, and the model consists of ResNet18, GRU and 3 fully connected layers. The data of 82 individuals are collected for training and testing. The mean absolute errors and standard deviations are 2.16 mmHg and 3.09 mmHg for diastolic blood pressure, 3.15 mmHg and 5.14 mmHg for systolic blood pressure, according with AAMI standard and BHS standard.

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备注/Memo

备注/Memo:
【收稿日期】2024-08-19 【基金项目】国家自然科学基金（62101148, 62361013）；广西自然科学基金（2021GXNSFBA220051）；广西自动检测技术与仪器重点实验室基金（YQ19114） 【作者简介】张锡壮，硕士研究生，研究方向：生物传感与智能仪器，E-mail: zhangxizhuang1021@163.com 【通信作者】梁永波，博士，副研究员，研究生导师，研究方向：生物信号处理与医学智能仪器，E-mail: liangyongbo@guet.edu.cn
更新日期/Last Update: 2024-11-26