A machine learning approach for non-invasive blood pressure estimation
Andrea Tiloca
A machine learning approach for non-invasive blood pressure estimation.
Rel. Danilo Demarchi, Guido Pagana. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2020
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Abstract
The increase in the production and adoption of wearable devices in everyday life, as well as the improvement of their refinement and precision, encourage the development prospects of this market in the future. The wearable market size in 2022 is estimated to reach 57,653 million dollars. Due to its continuous increase in easy to use, flexibility and convenience, it can grant the possibility of returning the collected data in real-time. Nevertheless, there are currently limits due to the life of their battery which is holding back their position on the market. In parallel, the increase in their computational capacity and the increase in the simplicity with which it is possible to integrate them with complex algorithms, such as machine learning algorithms encourage the study of new methods for estimating vital parameters with non-invasive techniques, in the topic of this work the blood pressure measurement.
For decades, cardiovascular diseases (CVD) have been a major cause of mortality and morbidity around the world [Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?] and the number of patients suffering from CVD increases year by year
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