Classification of Disorders in Vocal Folds Using Electroglottographic Signal

Mandal, Tanumay; Rao, K. Sreenivasa; Gupta, Sanjay Kumar

doi:10.21437/interspeech.2018-1967

Cited by 1 publication

(1 citation statement)

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“…The above table represents the features that are extracted from the EGG signal. [33][34][35][36] In the proposed study features are extracted for both normal data set that includes voiced and unvoiced data sets, and It is defined as the total energy of the signal based on Energy Spectral Density (ESD).…”

Section: Methodology/design Considerationsmentioning

confidence: 99%

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers

Sai

Rajalakshmi

Snekhalatha

2021

Proc Inst Mech Eng H

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Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such as Hyperthyroidism or Hypothyroidism. Electroglottography signal is a bio signal which represents the impedance that exist between the glottis regions. The study aims at design and development of an hardware circuit for the acquisition of Electroglottogram signal from normal and thyroid subjects is proposed followed by feature extraction from the acquired bio signal is performed. Further, machine learning classifiers were used to classify the normal and thyroid individuals. This modality of acquisition is non-invasive. Performance evaluation is done by testing various classifiers to study the accuracy. The classifiers tested were Random Forest, Random Tree, Bayes Net, Multilayer Perceptron, Simple Logistic classifier, and One-R classifier. Classifiers such as Random Forest, Random Tree, and Multilayer Perceptron showed high accuracy. The accuracy estimated by these classifiers was tested and its ROC curves with AUC scores were derived. The highest accuracy was reported for Simple Logistic classifier which was about 95.1%. Random Forest and Random Tree reported 93.5% and 91.9% respectively. Similarly, Multilayer Perceptron and Bayes Net gave 93.5% and 91.9%. The One-R classifier algorithm reported the lowest accuracy of 90.3% among the studied classifier algorithms. The ROC-AUC score for the classifiers were also reported to be more than 0.9 which is considered more promising and supports the acquisition and processing methodology. Hence the proposed technique can be efficiently used to diagnose thyroid non-invasively.

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Section: Methodology/design Considerationsmentioning

confidence: 99%