Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate...

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Bibliographic Details
Main Authors: Yekanth Ram Chalumuri, Jacob P. Kimball, Azin Mousavi, Jonathan S. Zia, Christopher Rolfes, Jesse D. Parreira, Omer T. Inan, Jin-Oh Hahn
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Sensors
Subjects:
hypovolemia
blood volume
machine learning
seismocardiogram
ballistocardiogram
wearables
Online Access:https://www.mdpi.com/1424-8220/22/4/1336

Internet

https://www.mdpi.com/1424-8220/22/4/1336

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