Classification of cardiovascular diseases using dysphonia measurement in speech

• Autorzy: Bourouhou Abdelhamid , Jilbab Abdelilah , Nacir Chafik , Hammouch Ahmed

Identyfikatory

DOI

10.29354/diag/132586

• Języki publikacji: EN

Abstrakty

EN

Cardiovascular disease is the leading cause of death worldwide. The diagnosis is made by non-invasive methods, but it is far from being comfortable, rapid, and accessible to everyone. Speech analysis is an emerging non-invasive diagnostic tool, and a lot of researches have shown that it is efficient in speech recognition and in detecting Parkinson's disease, so can it be effective for differentiating between patients with cardiovascular disease and healthy people? This present work answers the question posed, by collecting a database of 75 people, 35 of whom suffering from cardiovascular diseases, and 40 are healthy. We took from each one three vocal recordings of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii… ..). By measuring dysphonia in speech, we were able to extract 26 features, with which we will train three types of classifiers: the k-near-neighbor, the support vectors machine classifier, and the naive Bayes classifier. The methods were tested for accuracy and stability, and we obtained 81% accuracy as the best result using the k-near-neighbor classifier.

Słowa kluczowe

EN

cardiovascular disease; speech analysis; dysphonia measurement; classification methods; PCA features selection

PL

choroba układu krążenia; analiza mowy; dysfonia; pomiar; metody klasyfikacji; selekcja cech

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2021
• Tom: Vol. 22, No. 1
• Strony: 31--37
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Bourouhou Abdelhamid

• University Mohammed V, Ecole Normale Superieure de l'Enseignement Technique, Rabat, Morocco

autor

Jilbab Abdelilah

• University Mohammed V, Ecole Normale Superieure de l'Enseignement Technique, Rabat, Morocco

autor

Nacir Chafik

• University Mohammed V, Ecole Normale Superieure de l'Enseignement Technique, Rabat, Morocco

autor

Hammouch Ahmed

• University Mohammed V, Ecole Normale Superieure de l'Enseignement Technique, Rabat, Morocco

Bibliografia

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• 5. Ghassemian H, Kenari AR. Early detection of pediatric heart disease by automated spectral analysis of phonocardiogram in children. J. Inf. Syst. Telecommun. 2015; 3(2): 66-75.
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• 7. Levanon Y, Lossos-Shifrin L. Inventors; Google, assignee. Method and system for diagnosing pathological phenomenon using a voice signal 2008. US patent 7,398,213 B1.
• 8. Bonneh YS, Levanon Y, Dean-Pardo O, Lossos L, Adini Y. Abnormal speech spectrum and increased pitch variability in young autistic children. Front Hum Neurosci. 2011;4:237.
• 9. Uma Rani K, Holi MS. Automatic detection of neurological disordered voices using mel cepstral coefficients and neural networks. In: 2013 IEEE Pointof-Care Healthcare Technologies (PHT) 2013:76-79. Bangalore, India, 2013.
• 10. Titze Ingo. Phonation into a straw as a voice building exercise. Journal of Singing. 2000; 57: 27-28.
• 11. Cnockaert L, Schoentgen J, Auzou P, Ozsancak C, Defebvre L, Grenez F. Low-frequency vocal modulations in vowels produced by Parkinsonian subjects, Speech Communication. 2008;50(4):288- 300. https://doi.org/10.1016/j.specom.2007.10.003.
• 12. Little MA, McSharry PE, Hunter EJ, Spielman J, Ramig, LO. Suitability of dysphonia measurements for telemonitoring of Parkinson's Disease. IEEE Transactions on Biomedical Engineering. 2009;56(4):1015-1022. https://doi.org/10.1109/TBME.2008.2005954.
• 13. Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. IEEE Trans Biomed Eng. 2012;59(5):1264-1271. https://doi.org/10.1109/TBME.2012.2183367.
• 14. https://www.cuidevices.com/product/resource/cmm-3729ab-38308-tr.pdf.
• 15. Bourouhou A, Jilbab A, Nacir C, Hammouch A. Detection and localization algorithm of the S1 and S2 heart sounds. 2017 International Conference on Electrical and Information Technologies (ICEIT), Rabat. 2017:1-4 https://doi.org/10.1109/EITech.2017.8255217.
• 16. Bourouhou A, Jilbab A, Nacir C, Hammouch A. Comparison of classification methods to detect the Parkinson disease. 2016 International Conference on Electrical and Information Technologies (ICEIT), Tangiers, 2016:421-424. https://doi.org/10.1109/EITech.2016.7519634.
• 17. Bourouhou A, Jilbab A, Nacir C, Hammouch A. Heart Sounds classification for a medical diagnostic assistance. International Journal of Online and Biomedical Engineering (iJOE) 2019; 15(11): 88-103.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).