Application of fractal dimension on vestibular response signals for diagnosis of Parkinson's disease.
Identifieur interne : 001721 ( Main/Exploration ); précédent : 001720; suivant : 001722Application of fractal dimension on vestibular response signals for diagnosis of Parkinson's disease.
Auteurs : Z A Dastgheib [Canada] ; B. Lithgow ; Z. MoussaviSource :
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference [ 1557-170X ] ; 2011.
English descriptors
- KwdEn :
- MESH :
- diagnosis : Parkinson Disease.
- physiopathology : Vestibule, Labyrinth.
- Analysis of Variance, Discriminant Analysis, Fractals, Humans, Signal Processing, Computer-Assisted, Time Factors.
Abstract
In this paper, a novel method based on analysis of dynamic response of vestibular system for diagnosis of Parkinson's Disease (PD) is introduced. Electrovestibulography (EVestG) signals are recorded from the ear canal in response to a vestibular stimulus. EVestG signals are in fact the vestibular response modulated by more cortical brain signals. We used EVestG data of 20 patients with PD and 26 age-matched healthy controls recorded in a previous study. We calculated the Katz Fractal Dimension (FD) of the extracted timing signal of firings during contralateral and ipsilateral stimuli of both left and right ear. We used multivariate analysis of variance (MANOVA) to select pairs of features showing the most significant differences between the groups. Then, Linear and Quadratic Discriminant (LDA, QDA) classification algorithms were applied on the selected features. The results have shown above 77.27% accuracy. Given the small population of the subjects and the patients were at different stage of disease, the results encourage continuing exploration of the application of EVestG for PD diagnosis and perhaps as a quick and non-invasive screening tool.
DOI: 10.1109/IEMBS.2011.6091946
PubMed: 22256170
Affiliations:
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Lithgow</name></author><author><name sortKey="Moussavi, Z" sort="Moussavi, Z" uniqKey="Moussavi Z" first="Z" last="Moussavi">Z. 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Lithgow</name></author><author><name sortKey="Moussavi, Z" sort="Moussavi, Z" uniqKey="Moussavi Z" first="Z" last="Moussavi">Z. Moussavi</name></author></analytic><series><title level="j">Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference</title><idno type="ISSN">1557-170X</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Analysis of Variance</term><term>Discriminant Analysis</term><term>Fractals</term><term>Humans</term><term>Parkinson Disease (diagnosis)</term><term>Signal Processing, Computer-Assisted</term><term>Time Factors</term><term>Vestibule, Labyrinth (physiopathology)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Parkinson Disease</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Vestibule, Labyrinth</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Analysis of Variance</term><term>Discriminant Analysis</term><term>Fractals</term><term>Humans</term><term>Signal Processing, Computer-Assisted</term><term>Time Factors</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, a novel method based on analysis of dynamic response of vestibular system for diagnosis of Parkinson's Disease (PD) is introduced. Electrovestibulography (EVestG) signals are recorded from the ear canal in response to a vestibular stimulus. EVestG signals are in fact the vestibular response modulated by more cortical brain signals. We used EVestG data of 20 patients with PD and 26 age-matched healthy controls recorded in a previous study. We calculated the Katz Fractal Dimension (FD) of the extracted timing signal of firings during contralateral and ipsilateral stimuli of both left and right ear. We used multivariate analysis of variance (MANOVA) to select pairs of features showing the most significant differences between the groups. Then, Linear and Quadratic Discriminant (LDA, QDA) classification algorithms were applied on the selected features. The results have shown above 77.27% accuracy. Given the small population of the subjects and the patients were at different stage of disease, the results encourage continuing exploration of the application of EVestG for PD diagnosis and perhaps as a quick and non-invasive screening tool.</div></front></TEI><affiliations><list><country><li>Canada</li></country><region><li>Manitoba</li></region><settlement><li>Winnipeg</li></settlement><orgName><li>Université du Manitoba</li></orgName></list><tree><noCountry><name sortKey="Lithgow, B" sort="Lithgow, B" uniqKey="Lithgow B" first="B" last="Lithgow">B. Lithgow</name><name sortKey="Moussavi, Z" sort="Moussavi, Z" uniqKey="Moussavi Z" first="Z" last="Moussavi">Z. Moussavi</name></noCountry><country name="Canada"><region name="Manitoba"><name sortKey="Dastgheib, Z A" sort="Dastgheib, Z A" uniqKey="Dastgheib Z" first="Z A" last="Dastgheib">Z A Dastgheib</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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