Blood pressure class estimation using CNN-GRU model
Date Issued
2022
Author(s)
Kuzmanov, Ivan
Madevska Bogdanova, Ana
Abstract
Blood pressure (BP) estimation can add on great
value in emergency medicine, especially in case of mass casualty
situations. 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 (GRU) hybrid model, containing
both CNN and GRU layers. The used dataset is the publicly
available UCI Machine Learning Repository dataset. We have
achieved f1 score of 0.83, 0.73 and 0.74 respectively according
to the BP classes and 78% overall accuracy.
value in emergency medicine, especially in case of mass casualty
situations. 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 (GRU) hybrid model, containing
both CNN and GRU layers. The used dataset is the publicly
available UCI Machine Learning Repository dataset. We have
achieved f1 score of 0.83, 0.73 and 0.74 respectively according
to the BP classes and 78% overall accuracy.
Subjects
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