Characterizing early-stage Alzheimer through spatiotemporal dynamics of handwriting
| Autor: | Victoria Cristancho-Lacroix, Sonia Garcia-Salicetti, Mounim A. El-Yacoubi, Anne-Sophie Rigaud, Christian Kahindo |
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| Přispěvatelé: | Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Centre National de la Recherche Scientifique (CNRS), Département Electronique et Physique (EPH), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), ARMEDIA (ARMEDIA-SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Centre National de la Recherche Scientifique (CNRS)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Centre National de la Recherche Scientifique (CNRS), AP-HP - Hôpital Cochin Broca Hôtel Dieu [Paris], Maladie d'Alzheimer : marqueurs génétiques et vasculaires, neuropsychologies (EA 4468), Université Paris Descartes - Paris 5 (UPD5)-Groupe hospitalier Broca, Centre National de la Recherche Scientifique (CNRS), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Dynamic time warping
Computer science business.industry Applied Mathematics Pattern recognition 02 engineering and technology Clustering of time series 03 medical and health sciences 0302 clinical medicine ComputingMethodologies_PATTERNRECOGNITION Discriminative model Probabilistic modeling Handwriting Online handwriting Signal Processing Kinematic parameters 0202 electrical engineering electronic engineering information engineering Alzheimer 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Cursive [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing 030217 neurology & neurosurgery |
| Zdroj: | IEEE Signal Processing Letters IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2018, 25 (8), pp.1136-1140. ⟨10.1109/LSP.2018.2794500⟩ |
| ISSN: | 1070-9908 |
| DOI: | 10.1109/LSP.2018.2794500⟩ |
| Popis: | International audience; We propose an original approach for characterizing early Alzheimer, based on the analysis of online handwritten cursive loops. Unlike the literature, we model the loop velocity trajectory (full dynamics) in an unsupervised way. Through a temporal clustering based on K-medoids, with Dynamic Time Warping as dissimilarity measure, we uncover clusters that give new insights on the problem. For classification, we consider a Bayesian formalism that aggregates the contributions of the clusters, by probabilistically combining the discriminative power of each. On a dataset consisting of two cognitive profiles, Early-stage Alzheimer Disease and Healthy persons, each comprising 27 persons collected at Broca Hospital in Paris, our classification performance significantly outperforms the state of the art, based on global kinematic features |
| Databáze: | OpenAIRE |
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