Blood pressure classification using CNN-LSTM model

Date Issued
2022-09
Author(s)
Kuzmanov, Ivan
Vasilevska, Anastasija
Madevska Bogdanova, Ana
Lehocki, Fedor
Abstract
Blood pressure (BP) estimation can aid the triage process
and help prioritizing and helping injured, especially in a situation of
multiple casualties. The presented research aims to create a model for
BP class estimation using electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. We focus on developing a BP classification
model as a convolutional neural network (CNN) - gated recurrent unit
(LSTM) hybrid model, containing both CNN and LSTM layers. The
used dataset is the publicly available UCI Machine Learning Repository
dataset. We have achieved stable AUCROC for each class - 0.89, 0.83,
and 0.89 respectively and overall accuracy of 83%.
Subjects

electrocardiogram · p...

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