A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
Joint Authors
Wang, Ludi
Zhou, Wei
Zhou, Xiaoguang
Xing, Ying
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-07
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years.
As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest.
In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed.
The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation.
Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.
American Psychological Association (APA)
Wang, Ludi& Zhou, Wei& Xing, Ying& Zhou, Xiaoguang. 2018. A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1187675
Modern Language Association (MLA)
Wang, Ludi…[et al.]. A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram. Journal of Healthcare Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1187675
American Medical Association (AMA)
Wang, Ludi& Zhou, Wei& Xing, Ying& Zhou, Xiaoguang. A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1187675
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187675