Estimation of Real-time Blood Pressure during Motion Using Electrocardiography Waveform

被引:0
|
作者
Tseng, Hsien-Wei [1 ]
Lee, Yang-Han [2 ]
Huang, Chien-Da [3 ,4 ]
Chen, Yi-Lun [3 ,4 ]
Liu, Yanfang [1 ]
机构
[1] Longyan Univ, Sch Informat Engn, 1 Dongxiao North Rd, Longyan, Fujian, Peoples R China
[2] Tamkang Univ, Elect & Comp Engn, Dept Elect Engn, 151 Ying Chuan Rd, New Taipei, Taiwan
[3] Tamkang Univ, Elect & Comp Engn, Dept Elect & Comp Engn, 151 Ying Chuan Rd, New Taipei, Taiwan
[4] Tamkang Univ, Elect Engn, Taipei, Taiwan
关键词
electrocardiography (ECG); blood pressure; HRV; so and chan algorithm; biomedical electronics; wearable; HOLTER ECG; ALGORITHM;
D O I
10.18494/SAM.2018.1821
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Electrocardiography (ECG) is a safe, noninvasive test with a history of more than a hundred years of use. A physician can judge whether a person is healthy by virtue of the ECG waveform. According to statistics from the World Health Organization (WHO), cardiovascular disease (CVD) is number one among the ten major causes of death globally, and the probability of CVD increases with age for office workers with irregular lifestyles and for obese people. People know about their health because of physiological signals, and there is a close relationship between CVD and blood pressure. The most common method currently is self-health monitoring by measuring blood pressure with a home hematomanometer. However, it may be inconvenient to measure blood pressure with a traditional hematomanometer, and it may be especially difficult if blood pressure needs to be measured under dynamic conditions. In this study we aimed to measure blood pressure with a wearable 2-point ECG device and to monitor the most obvious characteristics of QRS (ventricular depolarization) duration and change in blood pressure to enable health monitoring at any time.
引用
收藏
页码:621 / 631
页数:11
相关论文
共 50 条
  • [1] Estimation of the Blood Pressure Waveform using Electrocardiography
    Landry, Cederick
    Peterson, Sean D.
    Arami, Arash
    2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, : 7060 - 7063
  • [2] Real-Time Error Estimation for Real-Time Motion Prediction
    Moore, D.
    Sawant, A.
    MEDICAL PHYSICS, 2015, 42 (06) : 3711 - 3711
  • [3] Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model
    Li, Yung-Hui
    Harfiya, Latifa Nabila
    Purwandari, Kartika
    Lin, Yue-Der
    SENSORS, 2020, 20 (19) : 1 - 19
  • [4] REAL-TIME MOTION ESTIMATION WITH MRI
    Liu, Wenyang
    Ruan, Dan
    2013 IEEE 10TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2013, : 808 - 811
  • [5] Real-Time Motion Estimation Using Spatiotemporal Filtering in FPGA
    Orchard, Garrick
    Thakor, Nitish V.
    Etienne-Cummings, Ralph
    2013 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS), 2013, : 306 - 309
  • [6] AMBULATORY (HOLTER) ELECTROCARDIOGRAPHY USING REAL-TIME ANALYSIS
    KENNEDY, HL
    WIENS, RD
    AMERICAN JOURNAL OF CARDIOLOGY, 1987, 59 (12): : 1190 - 1195
  • [7] A real-time motion estimation FPGA architecture
    Babionitakis, Konstantinos
    Doumenis, Gregory A.
    Georgakarakos, George
    Lentaris, George
    Nakos, Kostantinos
    Reisis, Dionysios
    Sifnaios, Ioannis
    Vlassopoulos, Nikolaos
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2008, 3 (1-2) : 3 - 20
  • [8] Motion Estimation in Real-Time with Optimisation Methods
    Bruhn, Andres
    IT-INFORMATION TECHNOLOGY, 2008, 50 (01): : 66 - 69
  • [9] Experimental system for real-time motion estimation
    Kolodko, J
    Vlacic, L
    PROCEEDINGS OF THE 2003 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM 2003), VOLS 1 AND 2, 2003, : 981 - 986
  • [10] Real-time compressive tracking with motion estimation
    Wu, Jiayun
    Chen, Daquan
    Yi, Rui
    2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), 2013, : 2374 - 2379