Médecine

Paramédical

Autres domaines


S'abonner

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features - 26/05/20

Doi : 10.1016/j.irbm.2019.11.004 
I. Hammami , L. Salhi , S. Labidi
 University of Tunis El Manar, Higher Institute of Medical Technologies of Tunis, Research Laboratory of Biophysics and Medical Technologies, 1006, Tunis, Tunisia 

Corresponding author. Full postal address: 9 Rue Zouhair Essafi, 1006, Tunis, Tunisia.9 Rue Zouhair EssafiTunis1006Tunisia

Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.

pages 11
Iconographies 10
Vidéos 0
Autres 0

Graphical abstract

Le texte complet de cet article est disponible en PDF.

Highlights

EMD-DWT two stage analysis of voice samples.
Temporal energy to select robust IMF.
Extraction of HOSs features from robust IMF.
Classification accuracy reached 100% for the classification of “Funktionelle Dysphonia vs. Rekurrensparse”.

Le texte complet de cet article est disponible en PDF.

Abstract

Background

The voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.

Method

In this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.

Results

The proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.

Conclusion

HOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.

Le texte complet de cet article est disponible en PDF.

Keywords : EMD-DWT analysis, High Order Statistics features (HOS), Pathological voices detection and classification, Saarbrücken Voice Database (SVD), Support vector machine (SVM)


Plan


© 2019  AGBM. Publié par Elsevier Masson SAS. Tous droits réservés.
Ajouter à ma bibliothèque Retirer de ma bibliothèque Imprimer
Export

    Export citations

  • Fichier

  • Contenu

Vol 41 - N° 3

P. 161-171 - juin 2020 Retour au numéro
Article précédent Article précédent
  • Multiparametric qMTI Assessment and Monitoring of Normal Appearing White Matter and Classified T1 Hypointense Lesions in Relapsing-Remitting Multiple Sclerosis
  • M. Fooladi, N. Riyahi Alam, H. Sharini, K. Firouznia, M. Shakiba, M.H. Harirchian
| Article suivant Article suivant
  • An Improved Method with High Anti-interference Ability for R Peak Detection in Wearable Devices
  • X. Gu, J. Hu, L. Zhang, J. Ding, F. Yan

Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.

Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’achat d’article à l’unité est indisponible à l’heure actuelle.

Déjà abonné à cette revue ?

;

Mon compte


Plateformes Elsevier Masson

Déclaration CNIL

EM-CONSULTE.COM est déclaré à la CNIL, déclaration n° 1286925.

En application de la loi nº78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés, vous disposez des droits d'opposition (art.26 de la loi), d'accès (art.34 à 38 de la loi), et de rectification (art.36 de la loi) des données vous concernant. Ainsi, vous pouvez exiger que soient rectifiées, complétées, clarifiées, mises à jour ou effacées les informations vous concernant qui sont inexactes, incomplètes, équivoques, périmées ou dont la collecte ou l'utilisation ou la conservation est interdite.
Les informations personnelles concernant les visiteurs de notre site, y compris leur identité, sont confidentielles.
Le responsable du site s'engage sur l'honneur à respecter les conditions légales de confidentialité applicables en France et à ne pas divulguer ces informations à des tiers.