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Prediction of Minimally Conscious State Responder Patients to Non-invasive Brain Stimulation Using Machine Learning Algorithms

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12661)

Abstract

The right matching of patients to an intended treatment is routinely performed by doctor and physicians in healthcare. Improving doctor’s ability to choose the right treatment can greatly speed up patient’s recovery. In a clinical study on Disorders of Consciousness patients in Minimal Consciousness State (MCS) have gone through transcranial Electrical Stimulation (tES) therapy to increase consciousness level. We have carried out the study of MCS patient’s response to tES therapy using as input the EEG data collected before the intervention. Different Machine Learning approaches have been applied to the Relative Band Power features extracted from the EEG. We aimed to predict tES treatment outcome from this EEG data of 17 patients, where 4 of the patients sustainably showed further signs of consciousness after treatment. We have been able to correctly classify with 95% accuracy the response of patients to tES therapy. In this paper we present the methodology as well as a comparative evaluation of the different employed classification approaches. Hereby we demonstrate the feasibility of implementing a novel informed Decision Support System (DSS) based on this methodological approach for the correct prediction of patients’ response to tES therapy in MCS.

Keywords

  • Artificial intelligence
  • Machine learning
  • Decision support systems

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Notes

  1. 1.

    https://clinicaltrials.gov/ct2/show/NCT03293316.

  2. 2.

    https://ichgcp.net/clinical-trials-registry/NCT03221413.

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Correspondence to Aureli Soria-Frisch .

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Rojas, A. et al. (2021). Prediction of Minimally Conscious State Responder Patients to Non-invasive Brain Stimulation Using Machine Learning Algorithms. In: , et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_39

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  • DOI: https://doi.org/10.1007/978-3-030-68763-2_39

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