Intelligent classification of electrolaryngograph signals

@article{Ritchings2001IntelligentCO,
  title={Intelligent classification of electrolaryngograph signals},
  author={R. T. Ritchings and Mark A. McGillion and C. J. Moore},
  journal={2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  year={2001},
  volume={2},
  pages={1715-1718 vol.2}
}
This paper describes a prototype system for the intelligent classification of electrolaryngograph (EGG) signals in order to provide an objective assessment of voice quality in patients at different stages of recovery after treatment for larynx cancer. The system extracts salient short-term and longterm time-domain and frequency-domain parameters from EGG signals taken from male patients steadily phonating the vowel /i/. The quality of these voices was also independently assessed by a speech and… CONTINUE READING

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Key Quantitative Results

  • The best overall MLP structure was a 20-25-7, using the parameters [G1, G2, G3, G4, G5, FHNNE, HLM, Mf0, SDf0, V+], and the results indicate that this MLP was able to distinguish between the seven abnormal groups with an accuracy of up to 92%.

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