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Publicação
Cuffless Blood Pressure Estimation
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Orientador(es)
Resumo(s)
The blood pressure is an important factor in the diagnosis and evaluation of several
diseases, such as acute myocardial infarction and stroke. This way, continuous monitorization
of this parameter is crucial to a correct health evaluation. The current methods,
like the oscillometric method, have some major drawbacks, that can influence the output
values or even make the measurements impossible. One example is the high frequency
evaluation of the blood pressure, in the standard used methods the process of measuring
can take up to 3 minutes, and a waiting time is necessary between consecutive measurements.
This dissertation presents two different cuffless solution to solve those problems. One
based on physical models of the human body, and the other using machine learning
techniques.
In the first solution seven models that correlate pulse transit time and blood pressure,
deducted by different authors, were tested to evaluate which one performed better. The
testes were performed in a custom dataset acquired at Fraunhofer AICOS and in clinical
environment, with two different devices (low cost device and medical grade device). The
results indicate that pulse transit time can be used to track blood pressure, the developed
device/method was evaluated as grade A based in the Standard IEEE 1708-2014.
The second solution it’s a proof of concept using a public database and three different
machine learning methods (Random Forest, Neural Network and AdaBoost). Two sets
of features are calculated from the ECG and PPG signals, one using TSFEL (spectral,
frequency and time domain features) and a total of 15 custom features. The proposed
method outperforms the methods presented in bibliography with mean absolute error of
3.6 mmHg and 2.0 mmHg to systolic and diastolic blood pressure respectively.
Descrição
Palavras-chave
Blood Pressure Cuffless Machine Learning Physiological Models Photoplethysmography Electrocardiogram