A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram. (7th March 2018)

• Record Type: Journal Article
• Authors: Wang, Ludi; Zhou, Wei; Xing, Ying; Zhou, Xiaoguang
• Other Names: Liu Chengyu Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2018(2018)
• Publication Date: 2018-03-07
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2018/7804243
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : 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.