Vestibular spontaneous response as a potential signature for Parkinson's disease

In this paper, we report on a new method for potential diagnosis of Parkinson's Disease (PD) based on the analysis of the spontaneous response of vestibular system recorded by Electrovestibulography (EVestG). EVestG data of 20 individuals with PD and 28 healthy controls were adopted from a previous study. The field potentials and their firing pattern in response to whole body tilt stimuli from both left and right ears were extracted. We investigated several statistical and fractal features of the field potentials and also their firing patterns. One-way analysis of variance (ANOVA) was used to select the features showing the most significant differences between individuals with PD and the age-matched controls. Linear Discriminant analysis classification was applied to every selected feature using a leave-one-out routine. The result of each feature's classifier was used in a heuristic weighted average voting system to diagnose PD patients. The weights of the voting system were the (posterior) probabilities calculated by the designed classifier to indicate a subject related to a specific class. The results show more than 97% accuracy for PD diagnosis. Given that the patients were at different stage of disease, the high accuracy of the results encourages the use of vestibular response for PD diagnosis as a plausible quick and non-invasive screening tool.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  O B White,et al.  Ocular motor deficits in Parkinson's disease. I. The horizontal vestibulo-ocular reflex and its regulation. , 1983, Brain : a journal of neurology.

[3]  L. Peyrin,et al.  Monoamines (norepinephrine, dopamine, serotonin) in the rat medial vestibular nucleus: endogenous levels and turnover , 2005, Journal of Neural Transmission.

[4]  Witold Kinsner,et al.  Multifractal characterization of the electromyogram signals in presence of fatigue , 1998, Conference Proceedings. IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.98TH8341).

[5]  Zahra Moussavi,et al.  Diagnosis of Parkinson’s disease using electrovestibulography , 2012, Medical & Biological Engineering & Computing.

[6]  H. Pasterkamp,et al.  Classification of Lung Sounds during Bronchial Provocation Using Waveform Fractal Dimensions , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Smith Pf,et al.  Pharmacology of the vestibular system. , 1994 .

[8]  Brian Lithgow,et al.  A Methodology for Detecting Field Potentials from the External Ear Canal: NEER and EVestG , 2012, Annals of Biomedical Engineering.

[9]  B. Lithgow,et al.  The Relationship between Electrovestibulography and Parkinson's Disease Severity , 2006, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  M. Hoehn,et al.  Parkinsonism , 1967, Neurology.

[11]  C. Darlington,et al.  Pharmacology of the vestibular system. , 2000, Bailliere's clinical neurology.

[12]  Zahra Moussavi,et al.  Application of fractal dimension on vestibular response signals for diagnosis of Parkinson's disease , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  Zahra Moussavi,et al.  The fractality of lung sounds: A comparison of three waveform fractal dimension algorithms , 2005 .

[14]  L A Beckett,et al.  Prevalence of parkinsonian signs and associated mortality in a community population of older people. , 1996, The New England journal of medicine.

[15]  J. Dejong,et al.  An investigation of Parkinson's disease , 1962, Neurology.

[16]  M. J. Katz,et al.  Fractals and the analysis of waveforms. , 1988, Computers in biology and medicine.

[17]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[18]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.