TY - JOUR A2 - Liu, Chengyu AU - Wang, Ludi AU - Zhou, Wei AU - Xing, Ying AU - Zhou, Xiaoguang PY - 2018 DA - 2018/03/07 TI - A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram SP - 7804243 VL - 2018 AB - 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. SN - 2040-2295 UR - https://doi.org/10.1155/2018/7804243 DO - 10.1155/2018/7804243 JF - Journal of Healthcare Engineering PB - Hindawi KW - ER -