Paper Info
Title
Deep Recurrent Neural Network for Extracting Pulse Rate Variability from Photoplethysmography During Strenuous Physical Exercise
Abstract
Pulse rate variability (PRV) extracted from photoplethysmography (PPG) signal is a promising surrogate for heart rate variability (HRV) and has shown its great potential in diagnosing cardiac dysfunctions and autonomic nervous system diseases. However, the accurate extraction of PRV during strenuous physical exercise faces enormous challenges due to PPG's extreme vulnerability to motion artifacts. In this work, we introduce a deep recurrent neural network (RNN) based on bidirectional Long-Short Term Memory Network (biLSTM) for accurate PPG cardiac period segmentation. After that, three important indexes for PRV are calculated, which are peak intervals, pulse intervals, and instantaneous heart rates (IHR). Comparison results with state-of-the-art methods on a dataset including 48 subjects show the promising performance of the proposed algorithm in PRV indexes estimation and recovery. To our best knowledge, this is the first time a deep learning-based algorithm been involved for extraction of PRV from seriously corrupted PPG signals.
Year
DOI
Venue
2019
10.1109/BIOCAS.2019.8918711
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Keywords
Field
DocType
heart rate variability (HRV),pulse rate variability (PRV),photoplethysmography (PPG),motion artifacts,physical exercise
Computer vision,Autonomic nervous system,Pattern recognition,Segmentation,Heart rate variability,Photoplethysmogram,Computer science,Recurrent neural network,Feature extraction,Pulse (signal processing),Artificial intelligence,Deep learning
Conference
ISSN
ISBN
Citations 
2163-4025
978-1-5090-0618-2
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ke Xu11392171.73
Xinyu Jiang288.27
Haoran Ren301.35
Xiangyu Liu432.73
Wei Chen59639.08