Automatic detection of high-voltage spindles for Parkinson's disease
Identifieur interne : 000431 ( Main/Exploration ); précédent : 000430; suivant : 000432Automatic detection of high-voltage spindles for Parkinson's disease
Auteurs : Vincent Vigneron [France] ; Tahir Qasim Syed [Pakistan] ; Hsin Chen [Taïwan]Source :
English descriptors
Abstract
Parkinson's disease is a progressive neurodegenerative disorder which can be characterized by several symptoms such as tremor, slowness of movements, bradykinesia/akinesia and absence of postural reflexes.. and affects 10 million people worldwide. This paper develops a novel strategy for treating patients with PD: silence High-Voltage-Spindle that resemble the pathophysiological β-waves and contribute to the development of b-waves. Silencing HVSs is expected to delay or even prevent the development of β-waves and thus the progression of PD motor symptoms. High-voltage spindles (HVSs) are observed during waking immobility of patients. In this study, the local field potentials collected from the lesioned and control rats on multiple channels were analyzed with an online detection algorithm to identify the characteristic oscillations of HVSs from the second-order statistical properties of the signals and the detection performance is investigated to obtain the optimal choices. These results provide further motivation for the real-time implementation of the automatic HVS detection systems with improved performance for pathophysiological and therapeutic applications to the thalamocortical network dysfunctions.
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This paper develops a novel strategy for treating patients with PD: silence High-Voltage-Spindle that resemble the pathophysiological β-waves and contribute to the development of b-waves. Silencing HVSs is expected to delay or even prevent the development of β-waves and thus the progression of PD motor symptoms. High-voltage spindles (HVSs) are observed during waking immobility of patients. In this study, the local field potentials collected from the lesioned and control rats on multiple channels were analyzed with an online detection algorithm to identify the characteristic oscillations of HVSs from the second-order statistical properties of the signals and the detection performance is investigated to obtain the optimal choices. These results provide further motivation for the real-time implementation of the automatic HVS detection systems with improved performance for pathophysiological and therapeutic applications to the thalamocortical network dysfunctions.</div></front></TEI><affiliations><list><country><li>France</li><li>Pakistan</li><li>Taïwan</li></country></list><tree><country name="France"><noRegion><name sortKey="Vigneron, Vincent" sort="Vigneron, Vincent" uniqKey="Vigneron V" first="Vincent" last="Vigneron">Vincent Vigneron</name></noRegion></country><country name="Pakistan"><noRegion><name sortKey="Syed, Tahir Qasim" sort="Syed, Tahir Qasim" uniqKey="Syed T" first="Tahir Qasim" last="Syed">Tahir Qasim Syed</name></noRegion></country><country name="Taïwan"><noRegion><name sortKey="Chen, Hsin" sort="Chen, Hsin" uniqKey="Chen H" first="Hsin" last="Chen">Hsin Chen</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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