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2020 | OriginalPaper | Chapter

Input Clinical Parameters for Cardiac Heart Failure Characterization Using Machine Learning

Authors : Ernesto Iadanza, Camilla Chilleri

Published in: Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices

Publisher: Springer International Publishing

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Abstract

Congestive Heart Failure (CHF) is a serious chronic cardiac condition that brings high risk of urgent hospitalization and could lead to death. In this work we show how all the input clinical parameters for classifying CHF using Machine Learning can be acquired. The requested input are Blood Pressure, Heart Rate, Brain Natriuretic Peptide, Electrocardiogram, Blood Oxygen Saturation, Height, Weight and Ejection Fraction. The next step will be designing a novel device and connecting it to our Machine Learning classifier. A particular attention will be put to the assessment of electromagnetic compatibility (EMC) with other devices, taking into account that this new device will be used in many different settings (home, outdoor, etc.).

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Title: Input Clinical Parameters for Cardiac Heart Failure Characterization Using Machine Learning
Authors: Ernesto Iadanza
Camilla Chilleri
Publisher: Springer International Publishing
Book: Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices
Print ISBN: 978-3-030-30635-9

Electronic ISBN: 978-3-030-30636-6

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-30636-6_45

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