Web proceedings papers


Ivan Kuzmanov , Anastasija Vasilevska , Ana Madevska-Bogdanova , Nevena Ackovska , Magdalena Kostoska and Fedor Lehocki


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%.


electrocardiogram, photoplethysmogram, blood pressure estimation, triage, LSTM , artificial neural network, deep learning, CNN-LSTM hybrid model