REM Behavior Disorder (RBD) is a serious risk factor for neurodegenerative diseases such as Parkinson’s disease (PD). We describe here a recurrent neural network (RNN) for classification of EEG data collected from RBD patients and healthy controls (HC) forming a balanced cohort of 118 subjects in which 50 % of the RBD patients eventually developed either PD or Lewy Body Dementia (LBD). In earlier work [1, 2], we implemented support vector machine classifiers (SVMs) using EEG mean spectral features to predict the course of disease in the dual HC vs. PD problem with an accuracy of 85 %. Although largely successful, this approach did not attempt to exploit the non-linear dynamic characteristics of EEG signals, which are believed to contain useful information. Here we describe an Echo State Network (ESN) classifier capable of processing the dynamic features of EEG power at different spectral bands. The inputs to the classifier are the time series of 1 second-averaged EEG power at several selected frequencies and channels. The performance of the ESN reaches 85 % test-set accuracy in the HC vs. PD problem using the same subset of channels and bands we selected in our prior work on this problem using SVMs.
- Echo state networks
- Parkinson’s disease
- Reservoir computing
This work partly supported by the Michael J. Fox Foundation within the project “Discovery of EEG biomarkers for Parkinson Disease and Lewy Body Dementia using advanced Machine Learning techniques”.
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
Soria-Frisch, A., Marin, J., Ibañez, D., Dunne, S., Grau, C., Ruffini, G., Rodrigues-Brazète, J., Postuma, R., Gagnon, J.-F., Montplaisir, J., Pascual-Leone, A.: Machine learning for a Parkinsons prognosis and diagnosis system based on EEG. In: Proceedings of the International Pharmaco-EEG Society Meeting, IPEG 2014, Leipzig, Germany (2014)
Soria-Frisch, A., Marin, J., Ibañez, D., Dunne, S., Grau, C., Ruffini, G., Rodrigues-Brazète, J., Postuma, R., Gagnon, J.-F., Montplaisir, J., Pascual-Leone, A.: Evaluation of EEG biomarkers for Parkinson disease and other Lewy body diseases based on the use of machine learning techniques (in prep)
Fantini, M.L., Gagnon, J.F., Petit, D., Rompr, S., Dcary, A., Carrier, J., Montplaisir, J.: Slowing of electroencephalogram in rapid eye movement sleep behavior disorder. Ann. Neurol. 53(6), 774–780 (2003)
Rodrigues Brazète, J., Gagnon, J.F., Postuma, R.B., Bertrand, J.A., Petit, D., Montplaisir, J.: Electroencephalogram slowing predicts neurodegeneration in rapid eye movement sleep behavior disorder. Neurobiol. Aging, pp. 74–81 (2016). doi:10.1016/j.neurobiolaging.2015.10.007.
Bengio, Y., Simard, P., Frasconi, P.: IEEE Trans. Neur. Netw. 5(2), 157–166 (1994)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks, GMD Report 148, German National Research Center for Information Technology (2001). Fourth revision, July 2013. http://minds.jacobs-university.de/sites/default/files/uploads/papers/EchoStatesTechRep.pdf
Lukoševičius, M.: A practical guide to applying echo state networks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 659–686. Springer, Heidelberg (2012)
Yildiz, I.B., Jaeger, H., Kiebel, S.J.: Re-visiting the echo state property. Neural Netw. 35, 1–9 (2012). ISSN 0893–6080
Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)
This work has been partially funded by The Michael J. Fox Foundation for Parkinsons Research under Rapid Response Innovation Awards 2013. The data was collected by Jacques Montplaisir’s team at the Center for Advanced Research in Sleep Medicine affiliated to the University of Montrèal, Hopital du Sacre-Coeur, Montrèal and provided within the scope of the aforementioned project.
Editors and Affiliations
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Ruffini, G., Ibañez, D., Castellano, M., Dunne, S., Soria-Frisch, A. (2016). EEG-driven RNN Classification for Prognosis of Neurodegeneration in At-Risk Patients. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_36
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44777-3
Online ISBN: 978-3-319-44778-0