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Multi-task learning for subthalamic nucleus identification in deep brain stimulation

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Abstract

Deep brain stimulation (DBS) of Subthalamic nucleus (STN) is the most successful treatment for advanced Parkinson’s disease. Localization of the STN through Microelectrode recordings (MER) is a key step during the surgery. However, it is a complex task even for a skilled neurosurgeon. Different researchers have developed methodologies for processing and classification of MER signals to locate the STN. Previous works employ the classical paradigm of supervised classification, assuming independence between patients. The aim of this paper is to introduce a patient-dependent learning scenario, where the predictive ability for STN identification at the level of a particular patient, can be used to improve the accuracy for STN identification in other patients. Our inspiration is the multi-task learning framework, that has been receiving increasing interest within the machine learning community in the last few years. To this end, we employ the multi-task Gaussian processes framework that exhibits state of the art performance in multi-task learning problems. In our context, we assume that each patient undergoing DBS is a different task, and we refer to the method as multi-patient learning. We show that the multi-patient learning framework improves the accuracy in the identification of STN in a range from 4.1 to 7.7%, compared to the usual patient-independent setup, for two different datasets. Given that MER are non stationary and noisy signals. Traditional approaches in machine learning fail to recognize accurately the STN during DBS. By contrast in our proposed method, we properly exploit correlations between patients with similar diseases, obtaining an additional information. This information allows to improve the accuracy not only for locating STN for DBS but also for other biomedical signal classification problems.

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Notes

  1. http://www.inomed.com.

  2. The interested reader can download this dataset from https://dl.dropboxusercontent.com/u/43310202/DB_UTP.rar.

  3. http://www.medtronic.com/.

  4. The parametric classifiers, KNN1, KNN3 and the SVM are implemented using the PRTOOLS toolbox obtained from http://www.prtools.org/. GPR is implemented using the Gaussian Process Toolbox from http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/gp/. GPC is implemented using the Gaussian Process Toolbox from http://www.gaussianprocess.org/gpml/code/matlab/doc/.

  5. The latent functions \(u_q^i({\mathbf {x}})\) share the same covariance \(k_q({\mathbf {x}}, {\mathbf {x}}')\), irrespectively of the value of i

  6. For simplicity, we assume all the task are evaluated at the same number of tests inputs, \(N_*\). This is mainly to avoid notation clutterness.

  7. The exact expression for (3) includes an additional scaling factor that was not included, since for a high value of p, that scaling factor makes the kernel goes to zero quickly. A detailed mathematical explanation of this phenomenon is given in [3].

  8. Strictly speaking, we also need the coefficients \(b_{d,d'}^q\) to lead to a positive semidefinite function for \(k_{d,d'}(\cdot , \cdot )\). This can be enforced by using \(b_{d,d'}^q = \sum _{i=1}^{R_q}a_{d,q}^ia_{d',q}^i\), and estimating \(a_{d,q}^i\) instead of \(b_{d,d'}^q\).

  9. We implement MC and ML using the MULTIGP Toolbox retrieved from http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/multigp/. We implement MI using software available at http://homepages.inf.ed.ac.uk/gsanguin/software.html.

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Acknowledgements

We would like to thank the MD Hans Carmona Villada and the Institute of Epilepsy and Parkinson of Eje Cafetero, who helped in organizing the database DB-UTP. We also acknowledge Dr. Enrique Guijarro, for providing us with the DB-UPV database. This research has been developed under the project with code 111056934461 financed by Colciencias. H.D Vargas Cardona author is funded by Colciencias under the program: formación de alto nivel para la ciencia, la tecnología y la innovación—Convocatoria 617 de 2013.

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Authors and Affiliations

  1. Doctorate Program in Engineering, Universidad Tecnológica de Pereira, Vereda La Julita, Pereira, Colombia

    Hernán Darío Vargas Cardona

  2. Faculty of Engineering, Universidad Tecnológica de Pereira, Vereda La Julita, Pereira, Colombia

    Mauricio A. Álvarez & Álvaro A. Orozco

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  1. Hernán Darío Vargas Cardona
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  2. Mauricio A. Álvarez
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  3. Álvaro A. Orozco
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Correspondence to Mauricio A. Álvarez.

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Vargas Cardona, H.D., Álvarez, M.A. & Orozco, Á.A. Multi-task learning for subthalamic nucleus identification in deep brain stimulation. Int. J. Mach. Learn. & Cyber. 9, 1181–1192 (2018). https://doi.org/10.1007/s13042-017-0640-5

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