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Predictive and diagnosis models of stroke from hemodynamic signal monitoring

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Abstract

This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 min of monitoring, to predict the exitus during the first 3 h of monitoring, and to predict the stroke recurrence in just 15 min of monitoring. Patients with difficult access to a CT scan and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtained from the real-time developed models are the following: stroke diagnosis around 98% precision (97.8% sensitivity, 99.5% specificity), exitus prediction with 99.8% precision (99.8% Sens., 99.9% Spec.), and 98% precision predicting stroke recurrence (98% Sens., 99% Spec.).

Graphical abstract depicting the complete process since a patient is monitored until the data collected is used to generate models

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Funding

We acknowledge support from the Spanish Ministry of Science and Innovation under project PID2019-110866RB-I00.

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Authors and Affiliations

  1. Department of Computer Architecture and Automation, Complutense University of Madrid, Madrid, Spain

    Luis García-Terriza, José L. Risco-Martín & José L. Ayala

  2. Stroke Care Unit, Hospital Universitario de La Princesa, Madrid, Spain

    Gemma Reig Roselló

Authors
  1. Luis García-Terriza
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  2. José L. Risco-Martín
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  3. Gemma Reig Roselló
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  4. José L. Ayala
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Correspondence to Luis García-Terriza.

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García-Terriza, L., Risco-Martín, J.L., Roselló, G.R. et al. Predictive and diagnosis models of stroke from hemodynamic signal monitoring. Med Biol Eng Comput 59, 1325–1337 (2021). https://doi.org/10.1007/s11517-021-02354-6

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  • DOI: https://doi.org/10.1007/s11517-021-02354-6

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