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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vosskuhl, J., Struber, D., Herrmann, C.S.: Non-invasive brain stimulation: a paradigm shift in understanding brain oscillations. Front. Hum. Neurosci. 12, 211 (2018)
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)
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)
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)
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). https://doi.org/10.1016/j.nicl.2020.102426
Wu, W., et al.: An electroencephalograph-ic signature predicts antidepressant response in major depression. Nat Biotechnol. 38(4), 439–447 (2020). https://doi.org/10.1038/s41587-019-0397-3
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). https://doi.org/10.1097/YCT.0000000000000557
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)
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). https://doi.org/10.1111/ejn.13508
Estraneo, A., et al.: Multicenter prospective study on predictors of short-term outcome in disorders of consciousness. Neurology 95(11), e1488–e1499 (2020)
Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1–3), 37–52 (1987)
Efron, B., Tibshirani, R.J.: An introduction to the bootstrap. CRC Press, Boca Raton (1994)
Cortes, C., Vapnik, V.: Support-vector networks . Mach. Learning 20(3), 273–297 (1995)
Breiman, L.: Random forest. Mach. Learn. 45(1), 5–32 (2001)
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). https://doi.org/10.1109/CBMS.2019.00065
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)
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). https://doi.org/10.1016/j.clinph.2018.11.013. Epub 2018 Dec 3 PMID: 30580247
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Rojas, A. et al. (2021). Prediction of Minimally Conscious State Responder Patients to Non-invasive Brain Stimulation Using Machine Learning Algorithms. In: Del Bimbo, A., 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
Download citation
DOI: https://doi.org/10.1007/978-3-030-68763-2_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68762-5
Online ISBN: 978-3-030-68763-2
eBook Packages: Computer ScienceComputer Science (R0)
