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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)


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.


  • Artificial intelligence
  • Machine learning
  • Decision support systems

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  • Vosskuhl, J., Struber, D., Herrmann, C.S.: Non-invasive brain stimulation: a paradigm shift in understanding brain oscillations. Front. Hum. Neurosci. 12, 211 (2018)

    CrossRef  Google Scholar 

  • Kuo, M.F., Paulus, W., Nitsche, M.A.: Therapeutic effects of non-invasive brain stimulation with direct currents (tCS) in neuropsychiatric diseases. Neuroimage 85, 948–960 (2014)

    CrossRef  Google Scholar 

  • Woo, C.W., Chang, L.J., Lindquist, M.A., Wager, T.D.: Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20(3), 365 (2017)

    CrossRef  Google Scholar 

  • Lefaucheur, J.P., et al.: Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clin. Neurophysiol. 128(1), 56–92 (2017)

    CrossRef  Google Scholar 

  • Martens, G., et al.: Behavioral and electro-physiological effects of network-based frontoparietal tDCS in patients with severe brain injury: a randomized controlled trial. NeuroImage. Clin. 28, 102426 (2020).

  • Wu, W., et al.: An electroencephalograph-ic signature predicts antidepressant response in major depression. Nat Biotechnol. 38(4), 439–447 (2020).

    CrossRef  Google Scholar 

  • Scangos, K.W., Weiner, R.D., Coffey, E.C., Krystal, A.D.: An electrophysio-logical biomarker that may predict treatment response to ECT. J ECT. 35(2), 95–102 (2019).

    CrossRef  Google Scholar 

  • Ovadia-Caro, S., Khalil, A.A., Sehm, B., Villringer, A., Nazarova, M.: Predicting the response to non-invasive brain stimulation in stroke. Front. Neurol. 10, 302 (2019)

    CrossRef  Google Scholar 

  • Hordacre, B., Moezzi, B., Goldsworthy, M.R., Rogasch, N.C., Ridding, M.C.: Resting state functional connectivity measures correlate with the response to anodal transcranial direct current stimulation. Eur J Neurosci 45, 837–845 (2017).

    CrossRef  Google Scholar 

  • Estraneo, A., et al.: Multicenter prospective study on predictors of short-term outcome in disorders of consciousness. Neurology 95(11), e1488–e1499 (2020)

    CrossRef  Google Scholar 

  • Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    Google Scholar 

  • Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1–3), 37–52 (1987)

    CrossRef  Google Scholar 

  • Efron, B., Tibshirani, R.J.: An introduction to the bootstrap. CRC Press, Boca Raton (1994)

    CrossRef  Google Scholar 

  • Cortes, C., Vapnik, V.: Support-vector networks . Mach. Learning 20(3), 273–297 (1995)

    MATH  Google Scholar 

  • Breiman, L.: Random forest. Mach. Learn. 45(1), 5–32 (2001)

    CrossRef  Google Scholar 

  • El Shawi, R., Sherif, Y., Al-Mallah, M., Sakr, S.: Interpretability in healthcare a comparative study of local machine learning inter-pretability techniques. In: IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), Cordoba, Spain, vol. 2019, 275–280 (2019).

    CrossRef  Google Scholar 

  • Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)

    MathSciNet  MATH  Google Scholar 

  • Bočková, M., Rektor, I.: (2019) Impairment of brain functions in Parkinson’s disease reflected by alterations in neural connectivity in EEG studies: a viewpoint. Clin. Neurophysiol. 130(2), 239–247 (2019). Epub 2018 Dec 3 PMID: 30580247

    CrossRef  Google Scholar 

  • van der Maaten, L.J.P.: Learning a parametric embedding by preserving local structure. In: Proceedings of the Twelfth International Conference on Artificial Intelligence & Statistics (AI-STATS), JMLR W&CP, vol. 5, pp. 384–391 (2009)

    Google Scholar 

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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.

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