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First Version of a Support System for the Medical Diagnosis of Pathologies in the Larynx

  • Conference paper
  • First Online:
Biomedical Engineering Systems and Technologies (BIOSTEC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1814))

  • 222 Accesses

Abstract

Voice pathologies are widespread in society. However, the exams are invasive and uncomfortable for the patient, depending on the doctor’s experience doing the evaluation. Classifying and recognizing speech pathologies in a non-invasive way using acoustic analysis saves time for the patient and the specialist while allowing analyzes to be objective and efficient. This work presents a detailed description of an aid system for diagnosing speech pathologies associated with the larynx. The interface displays the parameters that physicians use most to classify subjects: absolute Jitter, relative Jitter, absolute Shimmer, relative Shimmer, Harmonic to Noise Ratio (HNR) and autocorrelation. The parameters used for the classification of the model are also presented (relative Jitter, absolute Jitter, RAP jitter, PPQ5 Jitter, absolute Shimmer, relative Shimmer, shimmer APQ3, shimmer APQ5, fundamental frequency, HNR, autocorrelation, Shannon entropy, entropy logarithmic and subject’s sex), as well as the description of the entire pre-processing of the data (treatment of Outliers using the quartile method, then data normalization and, finally, application of Principal Component Analysis (PCA) to reduce the dimension). The selected classification model is Wide Neural Network, with an accuracy of 98% and AUC of 0.99.

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References

  1. Reid, J., Parmar, P., Lund, T., Aalto, D.K., Jeffery, C.C.: Development of a machine-learning based voice disorder screening tool. Am. J. Otolaryngol. 43(2), 103327 (2022). https://doi.org/10.1016/J.AMJOTO.2021.103327

    Article  Google Scholar 

  2. Martins, R.H.G., Santana, M.F., Tavares, E.L.M.: Vocal cysts: clinical, endoscopic, and surgical aspects. J. Voice 25(1), 107–110 (2011). https://doi.org/10.1016/J.JVOICE.2009.06.008

    Article  Google Scholar 

  3. Ding, H., Gu, Z., Dai, P., Zhou, Z., Wang, L., Wu, X.: Deep connected attention (DCA) ResNet for robust voice pathology detection and classification. Biomed. Signal Process. Control 70, 102973 (2021). https://doi.org/10.1016/J.BSPC.2021.102973

    Article  Google Scholar 

  4. Godino-Llorente, J.I., Gomez-Vilda, P., Blanco-Velasco, M.: Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Trans. Biomed. Eng. 53(10), 1943–1953 (2006). https://doi.org/10.1109/TBME.2006.871883

    Article  Google Scholar 

  5. Gidaye, G., Nirmal, J., Ezzine, K., Frikha, M.: Unified wavelet-based framework for evaluation of voice impairment. Int. J. Speech Technol.25(3), 527–548 (2022)https://doi.org/10.1007/s10772-022-09969-6

  6. Hegde, S., Shetty, S., Rai, S., Dodderi, T.: A survey on machine learning approaches for automatic detection of voice disorders. J. Voice 33(6), 947.e11-947.e33 (2019). https://doi.org/10.1016/J.JVOICE.2018.07.014

    Article  Google Scholar 

  7. Pakravan, M., Jahed, M.: Significant pathological voice discrimination by computing posterior distribution of balanced accuracy. Biomed. Signal Process. Control 73, 103410 (2022). https://doi.org/10.1016/J.BSPC.2021.103410

    Article  Google Scholar 

  8. Darouiche, M.S., El Moubtahij, H., Yakhlef, M.B., Tazi, E.B.: An automatic voice disorder detection system based on extreme gradient boosting classifier; an automatic voice disorder detection system based on extreme gradient boosting classifier. In: 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (2022) https://doi.org/10.1109/IRASET52964.2022.9737980

  9. Zhang, X.J., Zhu, X.C., Wu, D., Xiao, Z.Z., Tao, Z., Zhao, H.M.: Nonlinear features of bark wavelet sub-band filtering for pathological voice recognition. Eng. Lett. 29(1), 49–60 (2021)

    Google Scholar 

  10. Castellana, A., Carullo, A., Corbellini, S., Astolfi, A.: Discriminating pathological voice from healthy voice using cepstral peak prominence smoothed distribution in sustained vowel. IEEE Trans. Instrum. Meas. 67(3), 646–654 (2018). https://doi.org/10.1109/TIM.2017.2781958

    Article  Google Scholar 

  11. Omeroglu, A.N., Mohammed, H.M.A., Oral, E.A.: Multi-modal voice pathology detection architecture based on deep and handcrafted feature fusion. Eng. Sci. Technol. an Int. J. 36, 101148 (2022). https://doi.org/10.1016/J.JESTCH.2022.101148

    Article  Google Scholar 

  12. Salehi, P.: The separation of multi-class pathological speech signals related to vocal cords disorders using adaptation wavelet transform based on lifting scheme. Cumhur. Üniversitesi Fen Edeb. Fakültesi Fen Bilim. Derg. 36(6), 2371–2382 (2015)

    Google Scholar 

  13. Ankışhan, H.: A new approach for detection of pathological voice disorders with reduced parameters. Electrica 18(1), 60–71 (2018)

    Google Scholar 

  14. Hamdi, R., Hajji, S., Cherif, A.: Voice pathology recognition and classification using noise related features. Int. J. Adv. Comput. Sci. Appl. 9(11), 82–87 (2018). https://doi.org/10.14569/IJACSA.2018.091112

    Article  Google Scholar 

  15. Chen, L., Chen, J.: Deep neural network for automatic classification of pathological voice signals. J. Voice 36(2), 288.e15-288.e24 (2022). https://doi.org/10.1016/J.JVOICE.2020.05.029

    Article  Google Scholar 

  16. Mohammed, M.A., et al.: Voice pathology detection and classification using convolutional neural network model. Appl. Sci. 10(11), 3723 (2020). https://doi.org/10.3390/app10113723

    Article  Google Scholar 

  17. Zakariah, M., Ajmi Alotaibi, Y., Guo, Y., Tran-Trung, K., Elahi, M.M.: An analytical study of speech pathology detection based on MFCC and deep neural networks (2022) https://doi.org/10.1155/2022/7814952

  18. Ali, Z., Hossain, M.S., Muhammad, G., Sangaiah, A.K.: An intelligent healthcare system for detection and classification to discriminate vocal fold disorders. Futur. Gener. Comput. Syst. 85, 19–28 (2018). https://doi.org/10.1016/J.FUTURE.2018.02.021

    Article  Google Scholar 

  19. Hammami, I., Salhi, L., Labidi, S.: Voice Pathologies classification and detection using EMD-DWT analysis based on higher order statistic features. IRBM 41(3), 161–171 (2020). https://doi.org/10.1016/J.IRBM.2019.11.004

    Article  Google Scholar 

  20. Toutounchi, S.J.S., Eydi, M., Golzari, S.E., Ghaffari, M.R., Parvizian, N.: Vocal cord paralysis and its etiologies: a prospective study. J. Cardiovasc. Thorac. Res. 6(1), 47–50 (2014). https://doi.org/10.5681/jcvtr.2014.009

    Article  Google Scholar 

  21. Moorthy, S.S., Gupta, S., Laurent, B., Weisberger, E.C.: Management of airway in patients with laryngeal tumors. J. Clin. Anesth. 17(8), 604–609 (2005). https://doi.org/10.1016/J.JCLINANE.2004.12.019

    Article  Google Scholar 

  22. Trotti, A., et al.: Randomized trial of hyperfractionation versus conventional fractionation in T2 squamous cell carcinoma of the vocal cord (RTOG 9512). Int. J. Radiat. Oncol. 89(5), 958–963 (2014). https://doi.org/10.1016/J.IJROBP.2014.04.041

    Article  Google Scholar 

  23. Arens, C., Glanz, H., Kleinsasser, O.: Clinical and morphological aspects of laryngeal cysts (1997)

    Google Scholar 

  24. Aminoff, M.J., Dedo, H.H., Izdebski, K.: Clinical aspects of spasmodic dysphonia. Neurosurgery Psychiatry 41, 361–365 (1978). https://doi.org/10.1136/jnnp.41.4.361

    Article  Google Scholar 

  25. Roy, N.: Functional dysphonia. Curr Opin Otolaryngol Head Neck Surg 11(3), 144–148 (2003). https://doi.org/10.1097/00020840-200306000-00002

    Article  Google Scholar 

  26. Kosztyla-Hojna, B., Rogowski, M., Ruczaj, J., Pepinski, W., Lobaczuk-Sitnik, A.: An analysis of occupational dysphonia diagnosed in the North-East of Poland. Int. J. Occup. Med. Environ. Health 17(2) 2004

    Google Scholar 

  27. Sudhir, P.M., Chandra, P.S., Shivashankar, N., Yamini, B.K.: Comprehensive management of psychogenic dysphonia: a case illustration. J. Commun. Disord. 42(5), 305–312 (2009). https://doi.org/10.1016/J.JCOMDIS.2009.04.003

    Article  Google Scholar 

  28. Karki, P., Gurung, U., Baskota, D.: Fibroma of epiglottis. Nepal. J. ENT Head Neck Surg. 1(1), 19–20 (2010). https://doi.org/10.3126/njenthns.v1i1.4733

    Article  Google Scholar 

  29. Wycliffe, N.D., Grover, R.S., Kim, P.D., Simental, A., Jr.: Hypopharyngeal cancer. Top Magn Reson Imaging 18(4), 243–258 (2007). https://doi.org/10.1097/RMR.0b013e3181570c3f

    Article  Google Scholar 

  30. Epstein, S.S., Winston, P., Friedmann, I., Ormerod, F.C.: The vocal cord polyp. J. Laryngol. Otol. 71(10), 673–688 (1957). https://doi.org/10.1017/S0022215100052312

    Article  Google Scholar 

  31. Epstein, S.S., Winston, P.: Intubation Granuloma. J. Laryngol. Otol. 71(1), 37–48 (1957). https://doi.org/10.1017/S0022215100051549

    Article  Google Scholar 

  32. Jurkov, A.Y., Bahilin, V.M., Shustova, T.I., Alekseeva, N.S.: A crosscorrelation analysis of fluctuations in heart rate and breathing when diagnosing the autonomic disorders in patients with hypotonic type of functional dysphonia. Zhurnal Nevrol. i Psihiatr. Im. S.S. Korsakova 120(5), 60–66 (2020) https://doi.org/10.17116/jnevro202012005160

  33. Teixeira, J.P., Gonçalves, A.: Algorithm for jitter and shimmer measurement in pathologic voices. Procedia Comput. Sci. 100, 271–279 (2016). https://doi.org/10.1016/J.PROCS.2016.09.155

    Article  Google Scholar 

  34. Fernandes, J., Silva, L., Teixeira, F., Guedes, V., Santos, J., Teixeira, J.P.: Parameters for vocal acoustic analysis - cured database. Procedia Comput. Sci. 164, 654–661 (2019). https://doi.org/10.1016/J.PROCS.2019.12.232

    Article  Google Scholar 

  35. Hamdi, R., HAJJI, S., Cherif, A., Processing, S.: Recognition of pathological voices by Human Factor Cepstral Coefficients (HFCC). J. Comput. Sci. (2020)https://doi.org/10.3844/jcssp.2020.1085.1099

  36. Boersma, P.: Stemmen meten met Praat. Stem-, Spraak- en Taalpathologie 12(4), 237–251 (2004)

    MathSciNet  Google Scholar 

  37. Silva, L., et al.: Outliers treatment to improve the recognition of voice pathologies. Procedia Comput. Sci. 164, 678–685 (2019). https://doi.org/10.1016/J.PROCS.2019.12.235

    Article  Google Scholar 

  38. Teixeira, J.P., Alves, N., Fernandes, P.O.: Vocal acoustic analysis: ANN versos SVM in classification of dysphonic voices and vocal cords paralysis. Int. J. E-Health Med. Commun. 11 (2020) https://doi.org/10.4018/IJEHMC.2020010103

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Acknowledgments

The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), SusTEC (LA/P/0007/2021) and 2021.04729.BD.

Author information

Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politecnico de Braganca (IPB), 5300, Braganca, Portugal

    Joana Fernandes

  2. Faculdade de Engenharia da Universidade do Porto (FEUP), 4200-465, Porto, Portugal

    Joana Fernandes & Diamantino Freitas

  3. Research Center in Digitalization and Intelligent Robotics (CeDRI), Associate Laboratory for Sustainability and Technology (SusTEC), Polytechnic Institute of Bragança (IPB), Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    João Paulo Teixeira

Authors
  1. Joana Fernandes
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  2. Diamantino Freitas
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  3. João Paulo Teixeira
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Corresponding authors

Correspondence to Joana Fernandes , Diamantino Freitas or João Paulo Teixeira .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Caparica, Portugal

    Ana Cecília A. Roque

  2. Virginia Tech, Blacksburg, VA, USA

    Denis Gracanin

  3. University of Vienna, Vienna, Austria

    Ronny Lorenz

  4. University of Edinburgh, Edinburgh, UK

    Athanasios Tsanas

  5. Université de Montréal, Montréal, QC, Canada

    Nathalie Bier

  6. Instituto de Telecomunicações and Instituto Superior Técnico - Lisbon University, Lisbon, Portugal

    Ana Fred

  7. Nova School of Science and Technology and University of Lisbon, Caparica, Portugal

    Hugo Gamboa

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Fernandes, J., Freitas, D., Teixeira, J.P. (2023). First Version of a Support System for the Medical Diagnosis of Pathologies in the Larynx. In: Roque, A.C.A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2022. Communications in Computer and Information Science, vol 1814. Springer, Cham. https://doi.org/10.1007/978-3-031-38854-5_1

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  • DOI: https://doi.org/10.1007/978-3-031-38854-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-38853-8

  • Online ISBN: 978-3-031-38854-5

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