A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram
TLDR
A method for estimating systolic and diastolic BP based only on a PPG signal is developed, using the multitaper method (MTM) for feature extraction, and an artificial neural network (ANN) for estimation.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.read more
Citations
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Journal ArticleDOI
A review of machine learning techniques in photoplethysmography for the non-invasive cuff-less measurement of blood pressure
C El-Hajj,Panayiotis A. Kyriacou +1 more
TL;DR: A comprehensive review for non-invasive cuff-less blood pressure estimation using the PPG approach along with their challenges and limitations is provided.
Journal ArticleDOI
End-to-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism.
Heesang Eom,Dong-Seok Lee,Seungwoo Han,Yuli Sun Hariyani,Yuli Sun Hariyani,Yonggyu Lim,Illsoo Sohn,Kwang Suk Park,Cheolsoo Park +8 more
TL;DR: An end-to-end deep learning architecture using only raw signals without the process of extracting features to improve the BP estimation performance using the attention mechanism is proposed, showing the applicability of the proposed model as an analytical metric for BP estimation.
Journal ArticleDOI
End-to-End Blood Pressure Prediction via Fully Convolutional Networks
TL;DR: A cuffless BP prediction method based on a deep convolutional neural network (CNN) that can overcome the problems mentioned above and achieves excellent performance in predicting both systolic blood pressure and diastolicBlood pressure over other known approaches.
Journal ArticleDOI
Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only.
TL;DR: A deep neural network model capable of extracting 32 features exclusively from PPG signals for BP estimation has remarkably high accuracy on the largest BP database found in the literature, which shows its effectiveness compared to some prior works.
Journal ArticleDOI
An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach
Tasbiraha Athaya,Sunwoong Choi +1 more
TL;DR: In this article, a U-net deep learning architecture was proposed to estimate arterial BP waveform non-invasively using photoplethysmogram (PPG) signals.
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