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

P Vijay Sai1, T Rajalakshmi2, U Snekhalatha1

  • 1Department of Biomedical Engineering, college of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|June 28, 2021
PubMed
Summary

This study developed a non-invasive hardware circuit to acquire Electroglottogram signals for diagnosing thyroid disorders. Machine learning accurately classified individuals, with the Simple Logistic classifier achieving 95.1% accuracy.

Keywords:
Bio signalelectroglottogramfeature classificationfeature extractionperformance evaluationthyroid gland

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Thyroid disorders, like hyperthyroidism and hypothyroidism, arise from hormonal imbalances affecting metabolism.
  • Electroglottography (EGG) signals, reflecting glottal impedance, offer a non-invasive method for physiological assessment.

Purpose of the Study:

  • To design and develop a hardware circuit for Electroglottogram (EGG) signal acquisition.
  • To extract features from EGG signals and utilize machine learning classifiers for thyroid disorder diagnosis.

Main Methods:

  • Development of a non-invasive hardware circuit for EGG signal acquisition from normal and thyroid subjects.
  • Application of various machine learning classifiers including Random Forest, Random Tree, Bayes Net, Multilayer Perceptron, Simple Logistic, and One-R.
  • Performance evaluation using accuracy metrics and Receiver Operating Characteristic (ROC) curves with Area Under the Curve (AUC) scores.

Main Results:

  • The Simple Logistic classifier achieved the highest accuracy of 95.1%.
  • Random Forest and Multilayer Perceptron demonstrated high accuracy at 93.5%.
  • All tested classifiers showed promising ROC-AUC scores exceeding 0.9, validating the methodology.

Conclusions:

  • The proposed non-invasive EGG signal acquisition and processing technique effectively aids in diagnosing thyroid disorders.
  • Machine learning classification of EGG signals presents a viable and accurate approach for thyroid condition assessment.