Fast Cuffless Blood Pressure Classification with ECG and PPG signals using CNN-LSTM Models in Emergency Medicine
Date Issued
2022-05-23
Author(s)
Kuzmanov, Ivan
Madevska Bogdanova, Ana
Abstract
Cuffless blood pressure (BP) measurement is
gaining a lot of attention as a promising new technology that
can be embedded in a patch-like biosensor device. Electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms
are non-invasive by their nature - they can be recorded
without sending any electrical impulses to the human body.
These signals present different aspects of the cardiovascular
system, thus using both of the signals for blood pressure
classification seems like a viable strategy. Quick estimation
of the blood pressure during the triage process in cases of
natural disasters with many injured subjects, is an essential
measure for following the hemostability of the injured. The
main goal of this study is to develop a two-class classification
model (Hypotension and Nothypotension) for fast prediction
of the blood pressure category by utilizing ECG and PPG
signals, in order to detect a BP sudden drop. The developed
deep learning models are based on the LSTM architecture
and its variants, CNN-LSTM. We also conducted three class
classification model. The models were trained and tested
using the data from the UCI Machine Learning Repository
Cuff-Less Blood Pressure Estimation dataset with 12000
instances. The best result in the two-class model is AUROC
= 0.74.
gaining a lot of attention as a promising new technology that
can be embedded in a patch-like biosensor device. Electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms
are non-invasive by their nature - they can be recorded
without sending any electrical impulses to the human body.
These signals present different aspects of the cardiovascular
system, thus using both of the signals for blood pressure
classification seems like a viable strategy. Quick estimation
of the blood pressure during the triage process in cases of
natural disasters with many injured subjects, is an essential
measure for following the hemostability of the injured. The
main goal of this study is to develop a two-class classification
model (Hypotension and Nothypotension) for fast prediction
of the blood pressure category by utilizing ECG and PPG
signals, in order to detect a BP sudden drop. The developed
deep learning models are based on the LSTM architecture
and its variants, CNN-LSTM. We also conducted three class
classification model. The models were trained and tested
using the data from the UCI Machine Learning Repository
Cuff-Less Blood Pressure Estimation dataset with 12000
instances. The best result in the two-class model is AUROC
= 0.74.
Subjects
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