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Application of Normalized Compression Distance and Lempel-Ziv Jaccard Distance in Micro-electrode Signal Stream Classification for the Surgical Treatment of Parkinson’s Disease
Bifet, A., Pfahringer, B., Read, J., & Holmes, G. (2013). Efficient Data Stream Classification via Probabilistic Adaptive Windows. In Proceedings of the 28th Annual ACM Symposium on Applied Computing (pp. 801–806). New York, NY, USA: ACM. doi: 10.1145/2480362.248051610.1145/2480362.2480516
Bifet, A., Gavaldà, R., Holmes, G., & Pfahringer, B. (2018). Machine Learning for Data Streams with Practical Examples in MOA. MIT Press.10.7551/mitpress/10654.001.0001
Cilibrasi, R. (2007). Statistical Inference Through Data Compression (PhD thesis). Institute for Logic, Language and Computation, University of Amsterdam.
Cohen, A. R., & Vitanyi, P. M. B. (2015). Normalized Compression Distance of Multisets with Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(8), 1602–1614. doi: 10.1109/TPAMI.2014.237517510.1109/TPAMI.2014.2375175
Hebb, A. O., Zhang, J. J., Mahoor, M. H., Tsiokos, C., Matlack, C., Chizeck, H. J., & Pouratian, N. (2014). Creating the Feedback Loop. Neurosurgery Clinics of North America, 25(1), 187–204. doi: 10.1016/j.nec.2013.08.00610.1016/j.nec.2013.08.006
Hell, F., Köglsperger, T., Mehrkens, J., & Boetzel, K. (2018). Improving the Standard for Deep Brain Stimulation Therapy: Target Structures and Feedback Signals for Adaptive Stimulation. Current Perspectives and Future Directions. Cureus, 10(4). doi: 10.7759/cureus.246810.7759/cureus.2468
Kuhner, A., Schubert, T., Cenciarini, M., Wiesmeier, I. K., Coenen, V. A., Burgard, W., Weiller, C., et al. (2017). Correlations between Motor Symptoms across Different Motor Tasks, Quantified via Random Forest Feature Classification in Parkinson’s Disease. Frontiers in Neurology, 8:607. doi: 10.3389/fneur.2017.0060710.3389/fneur.2017.00607
Losing, V., Hammer, B., & Wersing, H. (2018). Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM). Knowledge and Information Systems, 54(1), 171–201. doi: 10.1007/s10115-017-1137-y10.1007/s10115-017-1137-y
Mamun, K. A., Mace, M., Lutman, M. E., Stein, J., Liu, X., Aziz, T., Vaidyana-than, R., et al. (2015). Movement decoding using neural synchronization and inter-hemispheric connectivity from deep brain local field potentials. Journal of Neural Engineering, 12(5), 056011. doi: 10.1088/1741-2560/12/5/05601110.1088/1741-2560/12/5/056011
Mohammed, A., Bayford, R., & Demosthenous, A. (2018). Toward adaptive deep brain stimulation in Parkinson’s disease: a review. Neurodegenerative Disease Management, 8(2), 115–136. doi: 10.2217/nmt-2017-005010.2217/nmt-2017-0050
O’Halloran, R., Kopell, B. H., Sprooten, E., Goodman, W. K., & Frangou, S. (2016). Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders. Frontiers in Psychiatry, 7:63. doi: 10.3389/fpsyt.2016.0006310.3389/fpsyt.2016.00063
Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., Schrag, A. L., et al. (2017). Parkinson disease. Nature Reviews Disease Primers, 3:17013. doi: 10.1038/nrdp.2017.1310.1038/nrdp.2017.13
Raff, E., & Nicholas, C. (2017). An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1007–1015). ACM Press. doi: 10.1145/3097983.309811110.1145/3097983.3098111
Rajpurohit, V., Danish, S. F., Hargreaves, E. L., & Wong, S. (2015). Optimizing computational feature sets for subthalamic nucleus localization in DBS surgery with feature selection. Clinical Neurophysiology, 126(5), 975–982. doi: 10.1016/j.clinph.2014.05.03910.1016/j.clinph.2014.05.039
Santaniello, S., Gale, J. T., & Sarma, S. V. (2018). Systems approaches to optimizing deep brain stimulation therapies in Parkinson’s disease. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 10(5), e1421. doi: 10.1002/wsbm.142110.1002/wsbm.1421
Shamir, R. R., Dolber, T., Noecker, A. M., Walter, B. L., & McIntyre, C. C. (2015). Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson’s Disease. Brain Stimulation, 8(6), 1025–1032. doi: 10.1016/j.brs.2015.06.00310.1016/j.brs.2015.06.003
Shamir, R. R., Duchin, Y., Kim, J., Patriat, R., Marmor, O., Bergman, H., Vitek, J. L., et al. (2018). Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery. Neuro-surgery. doi: 10.1093/neuros/nyy21210.1093/neuros/nyy212
Taghva, A. (2011). Hidden Semi-Markov Models in the Computerized Decoding of Microelectrode Recording Data for Deep Brain Stimulator Placement. World Neurosurgery, 75(5–6), 758–763.e4. doi: 10.1016/j.wneu.2010.11.00810.1016/j.wneu.2010.11.008
Teplitzky, B. A., Zitella, L. M., Xiao, Y., & Johnson, M. D. (2016). Model-Based Comparison of Deep Brain Stimulation Array Functionality with Varying Number of Radial Electrodes and Machine Learning Feature Sets. Frontiers in Computational Neuroscience, 10:58. doi: 10.3389/fncom.2016.0005810.3389/fncom.2016.00058
Trevathan, J. K., Yousefi, A., Park, H. O., Bartoletta, J. J., Ludwig, K. A., Lee, K. H., & Lujan, J. L. (2017). Computational Modeling of Neurotransmitter Release Evoked by Electrical Stimulation: Nonlinear Approaches to Predicting Stimulation-Evoked Dopamine Release. ACS Chemical Neuro-science, 8(2), 394–410. doi: 10.1021/acschemneuro.6b0031910.1021/acschemneuro.6b00319
Valsky, D., Marmor-Levin, O., Deffains, M., Eitan, R., Blackwell, K., Bergman, H., & Israel, Z. (2017). Stop! Border Ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery. Movement Disorders, 32(1), 70–79. doi: 10.1002/mds.2680610.1002/mds.26806
Wong, S., Baltuch, G. H., Jaggi, J. L., & Danish, S. F. (2009). Functional localization and visualization of the subthalamic nucleus from microelectrode recordings acquired during DBS surgery with unsupervised machine learning. Journal of Neural Engineering, 6(2), 026006. doi: 10.1088/1741-2560/6/2/02600610.1088/1741-2560/6/2/026006