Automatic assessment of the motor state of the Parkinson's disease patient--a case study

Diagnostic Pathology

Jaroslaw Slawek¹  Piotr Robowski¹  Pawel Zwan²  Katarzyna Kaszuba²  Bozena Kostek² 

[1] Department of Neurological-Psychiatric Nursing, Medical University of Gdansk, Gdansk, Poland;Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland

关键词: Rough sets;    Rule-based decision algorithms;    UPDRS;    Parkinson's disease;   

Others  :  808150
DOI  :  10.1186/1746-1596-7-18

 received in 2011-11-11, accepted in 2012-02-19,  发布年份 2012

【 摘 要 】

This paper presents a novel methodology in which the Unified Parkinson's Disease Rating Scale (UPDRS) data processed with a rule-based decision algorithm is used to predict the state of the Parkinson's Disease patients. The research was carried out to investigate whether the advancement of the Parkinson's Disease can be automatically assessed. For this purpose, past and current UPDRS data from 47 subjects were examined. The results show that, among other classifiers, the rough set-based decision algorithm turned out to be most suitable for such automatic assessment.

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【 授权许可】

   
2012 Kostek et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, Akay M, Dy J, Welsh M, Bonato P: Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors. IEEE Trans Inf Technol Biomed 2009, 13(6):864-873.
• [2]State of the art review the Unified Parkinson's Disease Rating Scale (UPDRS): status and recommendations [http://onlinelibrary.wiley.com/doi/10.1002/mds.10473/abstract] webciteMov Disord 2003, 18(7):738-750.
• [3]Goetz CG, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stebbins GT, Stern MB, Tilley BC, Dodel R, Dubois B, Holloway R, Jankovic J, Kulisevsky J, Lang AE, Lees A, Leurgans S, LeWitt PA, Nyenhuis D, Olanow CW, Rascol O, Schrag A, Teresi JA, Van Hilten JJ, LaPelle N: Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): process, format, and clinimetric testing plan. Mov Disord 2007, 22(1):41-47.
• [4]Aminian K, Najafi B: Capturing human motion using body-fixed sensors: outdoor measurement and clinical applications. Comput Animat Virt W 2004, 15:79-94.
• [5]Baga D, Fotiadis DI, Konitsiotis S, Maziewski P, Greenlaw R, Chaloglou D, Arrendondo MT, Robledo MG, Pastor MA, PERFORM: Personalised Disease Management for Chronic Neurodegenerative Diseases: The Parkinson's disease and amyotrophic lateral sclerosis cases. In eChallenges e-2009 Conference 21-23 October 2009; Istanbul. Edited by von Zedtwitz M. Istanbul; 2009.
• [6]Godfrey A, Conway R, Meagher D, Olaighin G: Direct measurement of human movement by accelerometry. Med Eng & Phys 2008, 30:1364-1386.
• [7]Shima K, Tsuji T, Kan E, Kandori A, Yokoe M, Sakoda S: Measurement and evaluation of finger tapping movements using magnetic sensors. Proc 30th Annual International IEEE EMBS Conference, 20-24 August 2008, Vancouver, Canada 2008, 5628-5631.
• [8]Lee SW, Mase K: Activity and location recognitions using wearable sensors. Pervasive Comput 2002, 1(3):24-32.
• [9]Greenlaw R, Robledo MG, Estrada JJ, Pansera M, Konitsiotis S, Baga D, Maziewski P, Pastor MA, Papasava A, Chaloglou D, Zanichelli F: PERFORM: Building and Mining Electronic Records of Neurological Patients Being Monitored in the Home. Munich: Tech. rep., World Congress on Medical Physics and Biomedical Engineering; 2009.
• [10]Ravi N, Dandekar N, Mysore P, Littman M: Activity recognition from accelerometer data. Artificial intelligence. Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence 2005, 1541-1546.
• [11]Lombriser C, Bharatula N, Troste G, Roggen D: On-body activity recognition in a dynamic sensor network. In Proceedings of the ICST 2nd International Conference on Body Area Networks, 17. Florence, Italy; 2007.
• [12]Mathie MJ, Coster ACF, Lovell NH, Celler BG: Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement. Physiol Meas 2004, 25:1-20.
• [13]Maziewski P, Kupryjanow A, Kaszuba K, Czyzewski A: Accelerometer signal pre-processing influence on human activity recognition. In 13th IEEE NTAV/SPA Conference:, 24-26 September 2009. Edited by Dabrowski A. Poznan; 2009:95-99.
• [14]Patel S, Chen B, Buckley T, Rednic R, McClure D, Tarsy D, Shih L, Dy J, Welsh M, Bonato P: Home monitoring of patients with Parkinson's disease via wearable technology and a web-based application. Conference Proceedings IEEE Engineering in Medicine and Biology Society 2010, 4411-4414.
• [15]Lemoyne R, Mastroianni T, Cozza M, Coroian C, Grundfest W: Imple-mentation of an iPhone for characterizing Parkinson's disease tremor through a wireless accelerometer application. Conference Proceedings IEEE Engineering in Medicine and Biology Society 2010, 4954-4958.
• [16]Barroso MC, Esteves JGP, Nunes TP, Silva LM, Faria AC, Melo PL: A telemedicine instrument for remote evaluation of tremor: design and initial applications in fatigue and patients with Parkinson's Disease. BioMedical Engineering OnLine 2011, 10:14. 1-17, doi:10.1186/1475-925X-10-14, http://www.biomedical-engineering-online.com/content/10/1/14 webcite BioMed Central Full Text
• [17]Tsanas A, Little MA, McSharry PE, Ramig LO: Accurate telemonitoring of Parkinson's disease progression by non-invasive speech tests. IEEE Trans Biomed Eng 2010, 57:884-893.
• [18]Tsanas A, Little MA, McSharry PE, Ramig LO: Enhanced classical dysphonia measures and sparse regression for telemonitoring of Parkinson's disease progression. In Proceedings of ICASSP'10. Dallas, Texas; 2010:594-597.
• [19]Tsanas A, Little MA, McSharry PE, Ramig LO: New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson's disease symptom severity. In In Proceedings of International Symposium on Nonlinear Theory and its Applications. Krakow, Poland; 2010:457-460.
• [20]Narayana S, Fox PT, Zhang W, Franklin C, Robin DA, Vogel D, Ramig LO: Neural correlates of efficacy of voice therapy in Parkinson's disease identified by performance-correlation analysis. Hum Brain Mapp 2010, 31(2):222-236.
• [21]Tsanas A, Little MA, McSharry PE, Ramig LO: Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity. J Roy Soc Interface 2010, 8:842-855.
• [22]Tsumoto S: Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model. Inf Sci: Int J 2004, 162(2):65-80.
• [23]Ryutaro I, Masayuki N: Knowledge discovery from medical data-base with multi-strategy approach. SIG-FAI J 2003, 51:31-36.
• [24]Leondes CT (Ed): Knowledge-based systems. London: Academic; 2000.
• [25]Katsis CD, Ganiatsas G, Fotiadis DI: An integrated telemedicine platform for the assessment of affective physiological states. Diagn Pathol 2006, 1:16. BioMed Central Full Text
• [26]Pawlak Z: Rough sets. Int J Inf Comput Sci 1982, 11(5):341-356.
• [27]Rough Set Exploration System [http://logic.mimuw.edu.pl/~rses/] webcite
• [28]WEKA exploration system [http://weka.sourceforge.net/doc/weka/classifiers/rules/package-summary.html] webcite
• [29]Sokolova M, Marchand M, Japkowicz N, Shawe-Taylor J: The decision list machine. In 17th Conference of the Canadian Society for Computational Studies of Intelligence: Advances in Neural Information Processing Systems: May 2004; Ontario, the MIT Press. Volume 15. Edited by Tawfik AY et. al. Winsdsor, Ontario, Canada; 2004::921-928.
• [30]Compton P, Edwards G, Kang B, Lazarus L, Malor R, Menzies T, Preston P, Srinivasan A, Sammut S: Ripple down rules: possibilities and limitations. University of New South Wales, PO Box 1, Kensington NSW, Australia 2033, Department of Chemical Pathology, St Vincent's Hospital, Darlinghurst NSW, Australia; 2010.
• [31]Richards D, Compton P: Combining Formal Concept Analysis and Ripple Down Rules to Support the Reuse of Knowledge. Madrid: Proc. Software Engineering Knowledge Engineering, (SEKE'97) 1997, 177-184.
• [32]Komorowski J, Polkowski L, Skowron A: Rough Sets: A Tutorial. In Rough Fuzzy Hybridization. A New Trend in Decision- Making. Edited by Pal SK, Skowron A. Springer Verlag; 1999.
• [33]Pawlak Z, Skowron A: Rudiments of rough sets. Inf Sci 2007, 177(1):3-27.
• [34]Junior Moshkov M, Skowron A, Suraj Z: Extracting Relevant Information about Reduct Sets from Data Tables. Transactions on Rough Sets. Berlin: Springer-Verlag Heidelberg; 2008:200-211. IX, 5390