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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