Parkinson’s Disease Prediction Based on Multistate Markov Models

  • Hariton Costin Grigore T. Popa University of Medicine and Pharmacy, Iasi
  • Oana Geman Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava Development and Human Health Department

Abstract

In the real medical world, there are many symptoms or chronic diseases that cannot be characterized in a deterministic way, and which must be examined in a random way. In the study of these stochastic processes, Markov chains are used. There is a wide variety of phenomena that suggest a behavior in a Markov process manner such as: the probability that a patient's health to improve, to get worse, to remain stable or to progress to death within a certain time slot, depending on what happened in the previous time window. Our goal is to show that the Markov chains can be applied to the patients with Parkinson’s disease in order to predict the evolution of the disease over time. So the doctor may decide a therapeutic solution that is adapted to the patient's needs, and that can improve the quality of the patient's life with Parkinson's disease in terminal stage.

Author Biography

Hariton Costin, Grigore T. Popa University of Medicine and Pharmacy, Iasi
Hariton Costin, BS in Electronics and Telecom, Ph.D. in Applied Informatics, MBA diploma, is full professor at the University of Medicine and Pharmacy/Faculty of Medical Bioengineering, Iasi, Romania. He is also senior researcher at the Romanian Academy–Iasi Branch, Institute of Computer Science, the Image Processing and Computer Vision Lab.Competence areas:medical electronics, biosignal and image processing, artificial intelligence, telemedicine and e-health.Scientific and research activity:about 110 published papers, 8 books, 4 book chapters, 3 patents, 2 national awards, 36 research reports, technical manager within FP5/INES 2001-32316 project, director of the first Romanian telemedical pilot center in Iasi, director for 9 granted projects in bioengineering, postdoc researcher at the USTL of Lille (France), invited talks at international conferences. Prof. Costin is a member of the IEEE-EMBS and of other 6 scientific societies. 

References

[1] National Parkinson Foundation, http://www.parkinson.org (2013).

[2] EuroPa, http://www.europarkinson.net (2013).

[3] National Institute of Neurological Disorders and Stroke, http://www.ninds.nih.gov/ (2013).

[4] Helmich, R.C.; Hallett, M.; Deuschl, G.; Toni, I.; Bloem, B.R. (2012); Cerebral Causes and Consequences of Parkinsonian Resting Tremor, Brain, 135(11): 3206-3226.

Cerebral Causes and Consequences of Parkinsonian Resting Tremor, Brain, 135(11): 3206-3226.
http://dx.doi.org/10.1093/brain/aws023

[5] Abdo, W.F.; Van de Warrenburg, B.P.; Quinn, N.P.; Bloem, B.R. (2010); The clinical approach to movement disorders, Nat. Rev. Neurol., 6: 29-37.

[6] Benninger, D.H.; Thees, S.; Kollias, S.S.; Bassetti, C.L.; Waldvogel, D. (2009); Morfological differences in Parkinson's disease with and without rest tremor, J. Neurol., 256: 256-263.

Morfological differences in Parkinson's disease with and without rest tremor, J. Neurol., 256: 256-263.
http://dx.doi.org/10.1007/s00415-009-0092-2

[7] Lees, A.J.; Hardy, J.; Revesz, T. (2009); Parkinson's disease, Lancet, 373: 2055-2066.

Parkinson's disease, Lancet, 373: 2055-2066.
http://dx.doi.org/10.1016/S0140-6736(09)60492-X

[8] Moran, R.J.; Mallet, N.; Litvak, V.; Dolan, R.J.; Magill, P.J.; Friston, K.J.; Brown, P. (2011); Alteration in brain connectivity underlying beta oscillations in Parkinsonism, Plos. Comput. Biol., 7: 102-124.

Alteration in brain connectivity underlying beta oscillations in Parkinsonism, Plos. Comput. Biol., 7: 102-124.

[9] Mure, H.; Hirano, S.; Tang, C.C.; Isaias, I.U.; Antonini, A.; Ma, Y.; Dhawan, V.; Eidelberg, D. (2011); Parkinson's disease tremor-related metabolic network : characterization, progression, and treatment effects, Neuroimage, 54: 1244-1253.

Parkinson's disease tremor-related metabolic network : characterization, progression, and treatment effects, Neuroimage, 54: 1244-1253.
http://dx.doi.org/10.1016/j.neuroimage.2010.09.028

[10] Shtilbans, A.; Henchcliffe, C. (2012); Biomarkers in Parkinson's Disease, Curr. Opin. Neurol., 25(4): 460-465.

Biomarkers in Parkinson's Disease, Curr. Opin. Neurol., 25(4): 460-465.
http://dx.doi.org/10.1097/WCO.0b013e3283550c0d

[11] Dorsey, E.R.; Constantinescu, R.; Thomson, R.P.; et al. (2007); Projected number of people with Parkinson's disease in most populous nations, 2005 through 2030, Neurology, 68: 384-386.

[12] Chahine, L.M.; Stern, M.B. (2012); Diagnostic markers for Parkinson's disease, Curr. Opin. Neurol., 24: 309-317.

Diagnostic markers for Parkinson's disease, Curr. Opin. Neurol., 24: 309-317.
http://dx.doi.org/10.1097/WCO.0b013e3283461723

[13] Stern, M.B.; Siderowf, A. (2010); Parkinson's at risk syndrome: can Parkinson's disease be predicted?, Mov. Disord., 25: 89-93.

Parkinson's at risk syndrome: can Parkinson's disease be predicted?, Mov. Disord., 25: 89-93.
http://dx.doi.org/10.1002/mds.22719

[14] Beck, J.R.; Pauker, S.G. (1983); The Markov Process in Medical Prognosis, Med. Decis. Making, 3(4): 419-435.

The Markov Process in Medical Prognosis, Med. Decis. Making, 3(4): 419-435.
http://dx.doi.org/10.1177/0272989X8300300403

[15] Magni, P., Quaglini, S.; Marchetti, M.; Barosi, G. (2000); Deciding when to intervene: a Markov decision process approach, International Journal of Medical Informatics, 60: 237-253.

Deciding when to intervene: a Markov decision process approach, International Journal of Medical Informatics, 60: 237-253.
http://dx.doi.org/10.1016/S1386-5056(00)00099-X

[16] Jackson, C.H.; Duffy, S.W.; Couto, Elisabeth (2003);

Multistate Markov models for disease progression with classification error, The Statistician, 52(2): 193-209.
http://dx.doi.org/10.1111/1467-9884.00351

[17] Mackel, T.; Rosen, J.;Pugh, C. (2007); Application of Hiden Markov Modeling to Objective Medical Skill Evaluation, Medicine Meets Virtual Reality, 15: 316-318.

Application of Hiden Markov Modeling to Objective Medical Skill Evaluation, Medicine Meets Virtual Reality, 15: 316-318.

[18] Rosen J., Chang, L.; Brown, J.; Sinanan, M.; Hannaford, B. (2006); Generalized Approach for modeling Minimally Invasive Surgery as a Stochastic Process Using a Discrete Markov Model, IEEE Transaction on Biomedical Engineering, 53: 399-413.

Generalized Approach for modeling Minimally Invasive Surgery as a Stochastic Process Using a Discrete Markov Model, IEEE Transaction on Biomedical Engineering, 53: 399-413.
http://dx.doi.org/10.1109/TBME.2005.869771

[19] Rabiner, L. (1989); A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, 77(2).
http://dx.doi.org/10.1109/5.18626

[20] Geman, O. (2011); Data Processing for Parkinson's Disease Tremor, Speech and Gait Signal Analysis, EHB'2011, IEEE E-Health and Bioengineering Conference, ISBN: 978-1-4577-0292-1, pp. 53-57.

[21] Pohoaţă, S.; Geman, Oana; Graur, A.(2012); Dual tasking: gait and tremor in Parkinson's disease – acquisition, processing and clustering, Proc. of the National Symposium of Theoretical Electrical Engineering, SNET 2012.

[22] Lemoyne, R.; Implementation of an iPhone for characterizing Parkinson's disease tremor through a wireless accelerometer application, 32 Annual Conference of the IEEE EMBS, pp. 4954-4958.

[23] Marimorita, M.; Iizuka, T.; Takeuchi, A.; Shirataka, M.; Ikeda, N. (2009); Development of a System for Measurement and Analysis of Tremor Using a Three-axis Accelerometer, Methods Inf. Med., 6: 589-594.

Development of a System for Measurement and Analysis of Tremor Using a Three-axis Accelerometer, Methods Inf. Med., 6: 589-594.

[24] Clark, R.A.; Bryant, A.L.; Pua, Y.; McCrory, P.; Bennell, K.; Hunt, M.; Validity and reliability of Nintendo Wii Balance Board for assessment of standing balance, Gait & Posture, 31: 307-310.
http://dx.doi.org/10.1016/j.gaitpost.2009.11.012

[25] Nintendo Wii™, http://www.nintendo.co.uk/

[26] Sonnenberg, A. (2009), Transposed Markov matrix as a new decision tool of how to choose among competing investment options in academic medicine, Computational and Mathematical Methods in Medicine, 10(1): 1-7.
http://dx.doi.org/10.1080/17486700701865323

[27] Sonnenberg, F.a.; Beck, J.R (1993)., Markov models in medical decision making: a practical guide, Med. Decis. Making, 13: 322-338.
http://dx.doi.org/10.1177/0272989X9301300409

[28] Bricq, S.; Collet, C.; Armspach, J.P. (2008); MS Lesion Segmentation based on Hidden Markov Chain, pp.2-6.

[29] Key, R. (1986); A Markov model for analyzing cancer markers and disease states in survival studies, Biometrics, 42: 855-865.
http://dx.doi.org/10.2307/2530699

[30] LeStrat, Y.; Carrat, F. (1999); Monitoring epidemiologic surveillance data using hidden Markov models, Statistics in Medicine, 18: 3463-3478.

Monitoring epidemiologic surveillance data using hidden Markov models, Statistics in Medicine, 18: 3463-3478.
http://dx.doi.org/10.1002/(SICI)1097-0258(19991230)18:24<3463::AID-SIM409>3.0.CO;2-I

[31] Zipkin, E.F.; Jennelle, C.S.; Cooch, E.G. (2010); A primer on the application of Markov chains to the study of wildlife disease dynamics, Methods in Ecology & Evolution, 1: 192-198.

[32] Craig, B.A.; Sendi, P.P. (2002); Estimation of the transition matrix of a discrete-time Markov chain, Health Economics, 11: 33-42.

Estimation of the transition matrix of a discrete-time Markov chain, Health Economics, 11: 33-42.
http://dx.doi.org/10.1002/hec.654

[33] Faddy, M.J.; McClean, S.I. (2005); Markov Chain Modelling for Geriatric Patient Care, Methods Inf. Med., 44: 369-373.

Markov Chain Modelling for Geriatric Patient Care, Methods Inf. Med., 44: 369-373.

[34] Geman, Oana; Turcu, C.O.; Graur, A. (2013); Parkinson's disease Screening Tools using a Fuzzy Expert System, Advances in Electrical and Computer Engineering, 13(1): 41-46.
Published
2013-07-12
How to Cite
COSTIN, Hariton; GEMAN, Oana. Parkinson’s Disease Prediction Based on Multistate Markov Models. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 8, n. 4, p. 525-537, july 2013. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/498>. Date accessed: 11 july 2020. doi: https://doi.org/10.15837/ijccc.2013.4.498.

Keywords

Parkinson’s disease; Markov chains; Multistate Markov Models; Prediction.