Two-stage feature selection of voice parameters for early Alzheimer's disease prediction

Autor: Victoria Cristancho-Lacroix, Christian Kahindo, Sonia Garcia-Salicetti, S. Mirzaei, J. Boudy, M. El Yacoubi, Anne-Sophie Rigaud, Hélène Kerhervé
Přispěvatelé: Département Electronique et Physique (EPH), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP)-Centre National de la Recherche Scientifique (CNRS), 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, Groupe hospitalier Broca, Université Paris Descartes - Paris 5 (UPD5), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP), CHU Cochin [AP-HP], 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), Hôpital Cochin [AP-HP]
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: IRBM
IRBM, Elsevier Masson, 2018, 39 (6), pp.430-435. ⟨10.1016/j.irbm.2018.10.016⟩
Innovation and Research in BioMedical engineering
Innovation and Research in BioMedical engineering, Elsevier Masson, 2018, 39 (6), pp.430-435. ⟨10.1016/j.irbm.2018.10.016⟩
ISSN: 1959-0318
DOI: 10.1016/j.irbm.2018.10.016⟩
Popis: International audience; Background: The goal of this work is to develop a non-invasive method in order to help detecting Alzheimer's disease in its early stages, by implementing voice analysis techniques based on machine learning algorithms. Methods: We extract temporal and acoustical voice features (e.g.Jitter and Harmonics-to-Noise Ratio) from read speech of patients in Early Stage of Alzheimer's Disease (ES-AD), with Mild Cognitive Impairment (MCI), and from a Healthy Control (HC) group. Three classification methods are used to evaluate the efficiency of these features, namely kNN, SVM and decision Tree. To assess the effectiveness of this set of features, we compare them with two sets of feature parameters that are widely used in speech and speaker recognition applications. A two-stage feature selection process is conducted to optimize classification performance. For these experiments, the data samples of HC, ES-AD and MCI groups were collected at AP-HP Broca Hospital, in Paris. Results: First, a wrapper feature selection method for each feature set is evaluated and the relevant features for each classifier are selected. By combining, By combining, for each classifier, the features selected from each initial set, we improve the classification accuracy by a relative gain of more than 30 % for all classifiers. Then the same feature selection procedure is performed anew on the combination of selected feature sets, resulting in an additional significant improvement of classification accuracy. Conclusion: The proposed method improved the classification accuracy for ES-AD, MCI and HC groups and promises the effectiveness of speech analysis and machine learning techniques to help detect pathological diseases
Databáze: OpenAIRE
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