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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Support vector wavelet adaptation for pathological voice assessment.

Nafise Erfanian Saeedi¹, Farshad Almasganj, Farhad Torabinejad

¹Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran. n_saeedi@aut.ac.ir

Computers in Biology and Medicine

|July 23, 2011

Summary

This study introduces a novel wavelet-based method for voice disorder detection. Utilizing genetic algorithms and support vector machines, it achieves 100% accuracy in distinguishing normal from pathological voices.

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Area of Science:

Biomedical Engineering
Signal Processing
Speech Science

Background:

Vocal system abnormalities impact voice quality and characteristics.
Digital analysis offers a non-invasive method for detecting voice alterations.
Accurate classification of pathological voices is crucial for diagnosis and treatment.

Purpose of the Study:

To develop and evaluate a wavelet-based method for distinguishing normal from pathological voices.
To utilize genetic algorithms for optimizing feature extraction parameters.
To achieve high classification accuracy for voice disorder detection.

Main Methods:

A wavelet filter bank approach was employed for feature extraction.
Support vector machines (SVMs) were used as classifiers.
Orthogonal filter banks were implemented using a lattice structure, parameterized for optimization.
A genetic algorithm was applied to find optimal filter bank parameters for perfect classification.

Main Results:

The proposed method successfully distinguished between normal and pathological voices.
A 100% correct classification rate was achieved on the KAY database and an additional test set.
The genetic algorithm effectively optimized filter bank parameters for accurate voice analysis.

Conclusions:

Wavelet-based digital voice analysis, optimized by genetic algorithms, is a highly effective tool for detecting voice disorders.
This non-invasive method demonstrates potential for clinical application in voice assessment.
The combination of wavelet filter banks and SVMs offers a robust solution for pathological voice classification.