Using Accelerometric and Gyroscopic Data to Improve Blood Pressure Prediction from Pulse Transit Time Using Recurrent Neural Network
Shrimanti Ghosh, Ankur Banerjee, Nilanjan Ray, Peter W. Wood, Pierre Boulanger, Raj Padwal
Abstract
We propose a method for estimating blood pressure (BP) non-invasively from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. This method has potential to be used as a continuous form of BP estimation. Along with these signals, to our knowledge, for the first time in the BP measurement studies, we included accelerometric and gyroscopic signals from a wearable device to compensate for motion during continuous BP prediction. Our prediction model is a long-short-term-memory (LSTM) architecture of a recurrent neural network (RNN), which accommodates the multiscale temporal dependency between the sequential raw signal values and the corresponding systolic and diastolic BP values. We performed a study with 50 healthy volunteers. The mean difference ± standard deviation (SD) of the RNN-based approach were 0.02±4.8 for SBP and 1.5±3.7 for DBP in seated position & 2.6±6.0 for SBP and 2.7±4.5 for DBP while walking. These values meet current validation standard requirements for measurement accuracy. Our experiments also demonstrate that the proposed RNN-based approach outperformed the classical linear regression model for BP prediction.
BibTeX
@inproceedings{icassp2018_usingacceleromet,
title = {Using Accelerometric and Gyroscopic Data to Improve Blood Pressure Prediction from Pulse Transit Time Using Recurrent Neural Network},
author = {Shrimanti Ghosh and Ankur Banerjee and Nilanjan Ray and Peter W. Wood and Pierre Boulanger and Raj Padwal},
booktitle = {ICASSP 2018},
year = {2018}
}