Wavelet-based characterization of gait signal for neurological abnormalities.
Identifieur interne : 000046 ( France/Analysis ); précédent : 000045; suivant : 000047Wavelet-based characterization of gait signal for neurological abnormalities.
Auteurs : E. Baratin [France] ; L. Sugavaneswaran [Canada] ; K. Umapathy [Canada] ; C. Ioana [France] ; S. Krishnan [Canada]Source :
- Gait & posture [ 1879-2219 ] ; 2015.
English descriptors
- KwdEn :
- Adult, Aged, Amyotrophic Lateral Sclerosis (diagnosis), Amyotrophic Lateral Sclerosis (physiopathology), Databases, Factual, Diagnosis, Computer-Assisted (methods), Female, Gait (physiology), Humans, Huntington Disease (diagnosis), Huntington Disease (physiopathology), Male, Middle Aged, Parkinson Disease (diagnosis), Parkinson Disease (physiopathology), Wavelet Analysis, Young Adult.
- MESH :
- diagnosis : Amyotrophic Lateral Sclerosis, Huntington Disease, Parkinson Disease.
- methods : Diagnosis, Computer-Assisted.
- physiology : Gait.
- physiopathology : Amyotrophic Lateral Sclerosis, Huntington Disease, Parkinson Disease.
- Adult, Aged, Databases, Factual, Female, Humans, Male, Middle Aged, Wavelet Analysis, Young Adult.
Abstract
Studies conducted by the World Health Organization (WHO) indicate that over one billion suffer from neurological disorders worldwide, and lack of efficient diagnosis procedures affects their therapeutic interventions. Characterizing certain pathologies of motor control for facilitating their diagnosis can be useful in quantitatively monitoring disease progression and efficient treatment planning. As a suitable directive, we introduce a wavelet-based scheme for effective characterization of gait associated with certain neurological disorders. In addition, since the data were recorded from a dynamic process, this work also investigates the need for gait signal re-sampling prior to identification of signal markers in the presence of pathologies. To benefit automated discrimination of gait data, certain characteristic features are extracted from the wavelet-transformed signals. The performance of the proposed approach was evaluated using a database consisting of 15 Parkinson's disease (PD), 20 Huntington's disease (HD), 13 Amyotrophic lateral sclerosis (ALS) and 16 healthy control subjects, and an average classification accuracy of 85% is achieved using an unbiased cross-validation strategy. The obtained results demonstrate the potential of the proposed methodology for computer-aided diagnosis and automatic characterization of certain neurological disorders.
DOI: 10.1016/j.gaitpost.2015.01.012
PubMed: 25661004
Affiliations:
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pubmed:25661004Le document en format XML
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Baratin</name><affiliation wicri:level="1"><nlm:affiliation>E.N.S. de l'Energie, l'Eau et l'Environnement, Laboratoire de Traitement du signal, de l'image et d'automatique, France. Electronic address: elisabeth.baratin@gmail.com.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>E.N.S. de l'Energie, l'Eau et l'Environnement, Laboratoire de Traitement du signal, de l'image et d'automatique</wicri:regionArea><wicri:noRegion>de l'image et d'automatique</wicri:noRegion><wicri:noRegion>de l'image et d'automatique</wicri:noRegion></affiliation></author><author><name sortKey="Sugavaneswaran, L" sort="Sugavaneswaran, L" uniqKey="Sugavaneswaran L" first="L" last="Sugavaneswaran">L. Sugavaneswaran</name><affiliation wicri:level="1"><nlm:affiliation>Department of Electrical and Computer Engineering, Ryerson University, Canada. 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Characterizing certain pathologies of motor control for facilitating their diagnosis can be useful in quantitatively monitoring disease progression and efficient treatment planning. As a suitable directive, we introduce a wavelet-based scheme for effective characterization of gait associated with certain neurological disorders. In addition, since the data were recorded from a dynamic process, this work also investigates the need for gait signal re-sampling prior to identification of signal markers in the presence of pathologies. To benefit automated discrimination of gait data, certain characteristic features are extracted from the wavelet-transformed signals. The performance of the proposed approach was evaluated using a database consisting of 15 Parkinson's disease (PD), 20 Huntington's disease (HD), 13 Amyotrophic lateral sclerosis (ALS) and 16 healthy control subjects, and an average classification accuracy of 85% is achieved using an unbiased cross-validation strategy. The obtained results demonstrate the potential of the proposed methodology for computer-aided diagnosis and automatic characterization of certain neurological disorders.</div></front></TEI><affiliations><list><country><li>Canada</li><li>France</li></country></list><tree><country name="France"><noRegion><name sortKey="Baratin, E" sort="Baratin, E" uniqKey="Baratin E" first="E" last="Baratin">E. Baratin</name></noRegion><name sortKey="Ioana, C" sort="Ioana, C" uniqKey="Ioana C" first="C" last="Ioana">C. Ioana</name></country><country name="Canada"><noRegion><name sortKey="Sugavaneswaran, L" sort="Sugavaneswaran, L" uniqKey="Sugavaneswaran L" first="L" last="Sugavaneswaran">L. Sugavaneswaran</name></noRegion><name sortKey="Krishnan, S" sort="Krishnan, S" uniqKey="Krishnan S" first="S" last="Krishnan">S. Krishnan</name><name sortKey="Umapathy, K" sort="Umapathy, K" uniqKey="Umapathy K" first="K" last="Umapathy">K. 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