The motor clinical hallmarks of Parkinson's disease (PD) are usually quantified by physicians using validated clinimetric scales such as the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, clinical ratings are prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a simple, inexpensive, and objective rating method. As a first step towards this goal, a triaxial accelerometer-based system was used in a sample of 36 PD patients and 10 age-matched controls as they performed the MDS-UPDRS finger tapping (FT) task. First, raw signals were epoched to isolate the successive single FT movements. Next, eighteen FT task movement features were extracted, depicting MDS-UPDRS features and accelerometer specific features. An ordinal logistic regression model and a greedy backward algorithm were used to identify the most relevant features in the prediction of MDS-UPDRS FT scores, given by 3 specialists in movement disorders (SMDs). The Goodman-Kruskal Gamma index obtained (0.961), depicting the predictive performance of the model, is similar to those obtained between the individual scores given by the SMD (0.870 to 0.970). The automatic prediction of MDS-UPDRS scores using the proposed system may be valuable in clinical trials designed to evaluate and modify motor disability in PD patients. 1. Introduction The most important functional disturbance in patients with Parkinson's disease (PD), a chronic neurodegenerative condition, is a disorder of voluntary movement prominently characterized by slowness. This phenomenon is generally called bradykinesia [1]. Tremor and muscle rigidity are also part of the motor phenotypic spectrum [2]. Although it has not been possible to define a single underlying pathophysiologic mechanism that explains everything, bradykinesia and other motor symptoms seem to be related to a progressive loss of dopaminergic neurons in the substantia nigra [2, 3]. Since decades, the medical community has been developing clinical tools such as rating scales to quantify the severity of motor and other symptoms in PD. Despite the various attempts to use instruments and devices for quantification, clinical scales remain the preferred method because they are easy to administer and widely available. In the late eighties, the Unified Parkinson's Disease Rating Scale (UPDRS) was proposed as the primary international rating scale for PD clinical care and research and is still anchored in the daily practice of MDs. The motor examination part of the UPDRS requires the Specialists in Movement Disorders
References
[1]
A. J. Espay, D. E. Beaton, F. Morgante, C. A. Gunraj, A. E. Lang, and R. Chen, “Impairments of speed and amplitude of movement in Parkinson's disease: a pilot study,” Movement Disorders, vol. 24, no. 7, pp. 1001–1008, 2009.
[2]
A. Bartels and K. Leenders, “Parkinson's disease: the syndrome, the pathogenesisa and pathophysiology,” Cortex, vol. 45, no. 8, pp. 915–921, 2009.
[3]
A. Schnitzler and J. Gross, “Normal and pathological oscillatory communication in the brain,” Nature Reviews Neuroscience, vol. 6, no. 4, pp. 285–296, 2005.
[4]
C. G. Goetz, B. C. Tilley, S. R. Shaftman et al., “Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results,” Movement Disorders, vol. 23, no. 15, pp. 2129–2170, 2008.
[5]
Movement Disorder Society, Movement Disorder Society Task Force on Rating Scales for Parkinson's Disease, “The Unified Parkinson's Disease Rating Scale (UPDRS): status and recommendations,” Movement Disorders, vol. 18, no. 7, pp. 738–750, 2003.
[6]
J. P. Giuffrida, D. E. Riley, B. N. Maddux, and D. A. Heldmann, “Clinically deployable kinesia? technology for automated tremor assessment,” Movement Disorders, vol. 24, no. 5, pp. 723–730, 2009.
[7]
A. Godfrey, R. Conway, D. Meagher, and G. óLaighin, “Direct measurement of human movement by accelerometry,” Medical Engineering and Physics, vol. 30, no. 10, pp. 1364–1386, 2008.
[8]
J. J. Kavanagh and H. B. Menz, “Accelerometry: a technique for quantifying movement patterns during walking,” Gait and Posture, vol. 28, no. 1, pp. 1–15, 2008.
[9]
R. A. Hyde, L. P. Ketteringham, S. A. Neild, and R. J. S. Jones, “Estimation of upper-limb orientation based on accelerometer and gyroscope measurements,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 2, pp. 746–754, 2008.
[10]
H. Zhou, T. Stone, H. Hu, and N. Harris, “Use of multiple wearable inertial sensors in upper limb motion tracking,” Medical Engineering and Physics, vol. 30, no. 1, pp. 123–133, 2008.
[11]
Z. Farkas, A. Csillik, I. Szirmai, and A. Kamondi, “Asymmetry of tremor intensity and frequency in Parkinson's disease and essential tremor,” Parkinsonism and Related Disorders, vol. 12, no. 1, pp. 49–55, 2006.
[12]
A. Salarian, H. Russmann, C. Wider, P. Burkhard, F. Vingerhoets, and K. Aminian, “Quantification of tremor and bradykinesia in Parkinson's disease using a novel ambulatory monitoring system,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, pp. 313–322, 2007.
[13]
D. E. Vaillancourt and K. M. Newell, “The dynamics of resting and postural tremor in Parkinson's disease,” Clinical Neurophysiology, vol. 111, no. 11, pp. 2046–2056, 2000.
[14]
S. M. Rissanen, M. Kankaanp??, M. P. Tarvainen et al., “Analysis of dynamic voluntary muscle contractions in Parkinson's disease,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 9, pp. 2280–2288, 2009.
[15]
R. Agostino, A. Currà, M. Giovannelli, N. Modugno, M. Manfredi, and A. Berardelli, “Impairment of individual finger movements in Parkinson's disease,” Movement Disorders, vol. 18, no. 5, pp. 560–565, 2003.
[16]
á. Jobbágy, P. Harcos, R. Karoly, and G. Fazekas, “Analysis of finger-tapping movement,” Journal of Neuroscience Methods, vol. 141, no. 1, pp. 29–39, 2005.
[17]
E. L. Stegem?ller, T. Simuni, and C. D. MacKinnon, “Effect of movement frequency on repetitive finger movements in patients with Parkinson's disease,” Movement Disorders, vol. 24, no. 8, pp. 1162–1169, 2009.
[18]
M. Yokoe, R. Okuno, T. Hamasaki, Y. Kurachi, K. Akazawa, and S. Sakoda, “Opening velocity, a novel parameter, for finger tapping test in patients with Parkinson's disease,” Parkinsonism and Related Disorders, vol. 15, no. 6, pp. 440–444, 2009.
[19]
D. A. Heldman, J. P. Giuffrida, R. Chen et al., “The modified bradykinesia rating scale for Parkinson's disease: reliability and comparison with kinematic measures,” Movement Disorders, vol. 26, no. 10, pp. 1859–1863, 2011.
[20]
A. J. Hughes, S. E. Daniel, L. Kilford, and A. J. Lees, “Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases,” Journal of Neurology Neurosurgery and Psychiatry, vol. 55, no. 3, pp. 181–184, 1992.
[21]
B. Caby, J. Stamatakis, P. Laloux, B. Macq, and Y. Vandermeeren, “Multimodal movement reconstruction for stroke rehabilitation and performance assessment,” Journal on Multimodal User Interfaces, vol. 4, no. 3, pp. 119–127, 2011.
[22]
J. Jankovic, “Pathophysiology and clinical assessment of parkinsonian symptoms and signs,” in Handbook of Parkinson's disease, R. Pahwa, K. E. Lyons, W. C. Koller, et al., Eds., pp. 71–108, 2003.
[23]
R. J. Elble, “Gravitational artifact in accelerometric measurements of tremor,” Clinical Neurophysiology, vol. 116, no. 7, pp. 1638–1643, 2005.
[24]
I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” The Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003.
[25]
Y. Saeys, I. Inza, and P. Larra?aga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007.
[26]
G. Carr, K. Hafner, and G. Koch, “Analysis of rank measures of association for ordinal data from longitudinal studies,” Journal of the American Statistical Association, vol. 84, no. 407, pp. 797–804, 1989.
[27]
E. W. Steyerberg, A. J. Vickers, N. R. Cook et al., “Assessing the performance of prediction models: a framework for traditional and novel measures,” Epidemiology, vol. 21, no. 1, pp. 128–138, 2010.