Abstract
There is high frequency incidence of depressive symptoms in neurodegenerative diseases (ND) but reasons for it is not well understood. Parkinson’s disease (PD) is often evoked by strong emotional event and related to reduced level of dopamine (reward hormone). Similarly to PD, in older (over 65 year of age) subjects with late onset Alzheimer’s disease (LOAD) have first symptoms related to depression (95%). Present work is devoted to the question if evaluation of depression can help to predict PD symptoms? We have gathered results of: neurological (disease duration, values of Unified Parkinson’s Disease Rating Scale (UPDRS)), neuropsychological (depression – Beck test, PDQ39 (life quality), Epworth (sleep problems)) and eye movement (RS – reflexive saccadic) tests. We have tested 24 PD patients only with medical treatment (BMT-group), and 23 PD with medical and recent DBS (deep brain stimulation DBS-group), and 15 older DBS (POP-group) treatments during one and half year with testing every six months (W1, W2, W3). From rules found with help of GC (RST-rough set theory) in BMTW1 (patients BMT during first visit W1) we have predicted UPDRS in BMTW2 and BMTW3 with accuracies (acc.) 0.765 (0.7 without Beck result) and 0.8 (0.7 without Beck result). By using BMTW1 rules we could predict disease progression (UPDRS) of another group of patients – DBSW1 group with accuracy of 0.765 but not DBSW2/W3 patients. By using DBSW2 rules we could predict UPDRS of DBSW3 (acc. = 0.625), POPW1 (acc. = 0.77), POPW2 (acc. = 0.5), POPW3 (acc. = 0.33). By adding depression attribute and by using GC we could make better predictions of disease progressions in many different groups of patients than without it.
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Reijnders, J.S., Ehrt, U., Weber, W.E., et al.: A systematic review of prevalence studies of depression in Parkinson’s disease. Mov. Disord. 23, 183–189 (2008)
Schrag, A., Horsfall, L., Walters, K., Noyce, A.: Petersen I prediagnotic presentations of Parkinson’s disease in primary care: a case-control study. Lancet Neurol. 14(1), 57–64 (2015)
Darweesh, S.K.L., et al.: Trajectories of prediagnostic functioning in Parkinson’s disease. Brain 140, 429–441 (2017)
Remy, P., Doder, M., Lees, A., Turjanski, N., Brook, D.: Depression in Parkinson’s disease: loss of dopamine and noradrenaline innervation in the limbic system. Brain 128(Pt 6), 1314–1322 (2005)
GPDS Steering Committee: Factors impacting on quality of life in Parkinson’s disease: results from an international survey. Mov. Disord. 17, 60–67 (2002)
Ishihara, L., Brayne, C.: A systematic review of depression and mental illness preceding Parkinson’s disease. Acta Neurol. Scand. 113, 211–220 (2006)
Przybyszewski, A.W.: Logical rules of visual brain: from anatomy through neurophysiology to cognition. Cognit. Syst. Res. 11, 53–66 (2010)
Przybyszewski, A.W.: The neurophysiological bases of cognitive computation using rough set theory. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 287–317. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89876-4_16
Przybyszewski, A.W., Kon, M., Szlufik, S., Szymanski, A., Koziorowski, D.M.: Multimodal learning and intelligent prediction of symptom development in individual parkinson’s patients. Sensors 16(9), 1498 (2016). https://doi.org/10.3390/s16091498
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht (1991)
Bazan, J., Nguyen, S.H., Nguyen, T.T., Skowron, A., Stepaniuk, J.: Decision rules synthesis for object classification. In: Orłowska, E. (ed.) Incomplete Information: Rough Set Analysis, pp. 23–57. Physica–Verlag, Heidelberg (1998)
Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag, Heidelberg (2000)
Grzymała-Busse, J.: A new version of the rule induction system LERS. Fundamenta Informaticae 31(1), 27–39 (1997)
Bazan, J.G., Szczuka, M.: The rough set exploration system. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005). https://doi.org/10.1007/11427834_2
Bazan, J.G., Szczuka, M.: RSES and RSESlib - a collection of tools for rough set computations. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 106–113. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45554-X_12
Przybyszewski, A.W., Szlufik, S., Habela, P., Koziorowski, D.M.: Rules determine therapy-dependent relationship in symptoms development of Parkinson’s disease patients. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10752, pp. 436–445. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75420-8_42
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This study was carried out in accordance with the recommendations of Bioethics Committee of Warsaw Medical University with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The Bioethics Committee of Warsaw Medical University approved the protocol.
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Przybyszewski, A.W., Nowacki, J.P., Drabik, A., Szlufik, S., Habela, P., Koziorowski, D.M. (2019). Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson’s Disease Patients. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_51
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DOI: https://doi.org/10.1007/978-3-030-14802-7_51
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