A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram.

Journal: Journal of healthcare engineering
PMID: 29707186

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.

Authors

  • Ludi Wang
    Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Ying Xing
    Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Xiaoguang Zhou
    Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China.

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