Automatic diagnosis of vocal fold paresis by employing phonovibrogram features and machine learning methods

Authors:
Daniel Voigt;Michael Döllinger;Anxiong Yang;Ulrich Eysholdt;Jörg Lohscheller
Affiliations:
Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Bohlenplatz 21, D-91054 Erlangen, Germany;Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Bohlenplatz 21, D-91054 Erlangen, Germany;Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Bohlenplatz 21, D-91054 Erlangen, Germany;Department of Phoniatrics and Pediatric Audiology, University Hospital Erlangen, Bohlenplatz 21, D-91054 Erlangen, Germany;University of Applied Science Trier, Department of Computer Science, Schneidershof, D-54293 Trier, Germany
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

Citing 12
Cited 3

Evolution strategies –A comprehensive introduction

Natural Computing: an international journal
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples

Journal of Biomedical Informatics
Multiple feature sets based categorization of laryngeal images

Computer Methods and Programs in Biomedicine
The class imbalance problem: A systematic study

Intelligent Data Analysis
Automated speech analysis applied to laryngeal disease categorization

Computer Methods and Programs in Biomedicine
A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images

Journal of Biomedical Informatics
A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy

Artificial Intelligence in Medicine
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Towards a computer-aided diagnosis system for vocal cord diseases

Artificial Intelligence in Medicine
Classification of functional voice disorders based on phonovibrograms

Artificial Intelligence in Medicine
A comparison of methods for multiclass support vector machines

IEEE Transactions on Neural Networks

Computer-aided diagnosis system: A Bayesian hybrid classification method

Computer Methods and Programs in Biomedicine
Using image processing technology combined with decision tree algorithm in laryngeal video stroboscope automatic identification of common vocal fold diseases

Computer Methods and Programs in Biomedicine
Using image processing technology and mathematical algorithm in the automatic selection of vocal cord opening and closing images from the larynx endoscopy video

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The clinical diagnosis of voice disorders is based on examination of the rapidly moving vocal folds during phonation (f0: 80-300Hz) with state-of-the-art endoscopic high-speed cameras. Commonly, analysis is performed in a subjective and time-consuming manner via slow-motion video playback and exhibits low inter- and intra-rater reliability. In this study an objective method to overcome this drawback is presented being based on Phonovibrography, a novel image analysis technique. For a collective of 45 normophonic and paralytic voices the laryngeal dynamics were captured by specialized Phonovibrogram features and analyzed with different machine learning algorithms. Classification accuracies reached 93% for 2-class and 73% for 3-class discrimination. The results were validated by subjective expert ratings given the same diagnostic criteria. The automatic Phonovibrogram analysis approach exceeded the experienced raters' classifications by 9%. The presented method holds a lot of potential for providing reliable vocal fold diagnosis support in the future.