Advertisement

Assessment of Therapeutic Progress After Acquired Brain Injury Employing Electroencephalography and Autoencoder Neural Networks

  • Adam KurowskiEmail author
  • Andrzej Czyżewski
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 876)

Abstract

A method developed for parametrization of EEG signals gathered from participants with acquired brain injuries is shown. Signals were recorded during therapeutic session consisting of a series of computer assisted exercises. Data acquisition was performed in a neurorehabilitation center located in Poland. The presented method may be used for comparing the performance of subjects with acquired brain injuries (ABI) who are involved in concentration training program. It may also allow for an assessment of relative difference in performance of two participants involved to exercises by comparing parameters derived from EEG signals acquired in the course of therapeutic sessions. The parametrization method is based on autoencoder neural networks. The efficiency of parameters extracted employing the algorithm was compared to parameters derived from the spectrum of EEG signal. As it was confirmed by achieved results, the presented autoencoder-based method may be applied to predict ABI subjects’ performance in attention training sessions.

Keywords

Acquired brain injuries Unsupervised learning Neurorehabilitation Electroencephalography Autoencoder neural network 

Notes

Acknowledgments

The project was funded by the National Science Centre on the basis of the decision number DEC-2014/15/B/ST7/04724.

References

  1. 1.
    O’Neill, B.R., Handler, M.H., Tong, S., Chapman, K.E.: Incidence of seizures on continuous EEG monitoring following traumatic brain injury in children. J. Neurosurg. Pediatr. 16(2), 167–176 (2015).  https://doi.org/10.3171/2014.12.peds14263CrossRefGoogle Scholar
  2. 2.
    Ronne-Engstrom, E., Winkler, T.: Continuous EEG monitoring in patients with traumatic brain injury reveals a high incidence of epileptiform activity. Acta Neurol. Scand. 114, 47–53 (2006).  https://doi.org/10.1111/j.1600-0404.2006.00652.xCrossRefGoogle Scholar
  3. 3.
    Gogna, A., Majumdar, A., Ward, R.: Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals. IEEE Trans. Biomed. Eng. 64, 2196–2205 (2015).  https://doi.org/10.1109/TBME.2016.2631620CrossRefGoogle Scholar
  4. 4.
    Way, G.P., Casey, S.G.: Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. Pac. Symp. Biocomput. 23, 80–91 (2018)Google Scholar
  5. 5.
    Mason, R., Gunst, R., Hess, J.: Statistical Design and Analysis of Experiments: with Applications to Engineering Science, 2nd edn. Wiley, Chicester (2003)CrossRefGoogle Scholar
  6. 6.
    Jennett, B.: The glasgow coma scale: history and current practice. Trauma 4, 91–103 (2002).  https://doi.org/10.1191/1460408602ta233oaCrossRefGoogle Scholar
  7. 7.
    Kingma, D., Adam, J.B.: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  8. 8.
    Webb, A.R.: Statistical Pattern Recognition, 2nd edn. Wiley, Chichester (2003)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Electronics, Telecommunications and Informatics, Audio Acoustics LaboratoryGdansk University of TechnologyGdanskPoland
  2. 2.Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems DepartmentGdansk University of TechnologyGdanskPoland

Personalised recommendations