Discrimination between patients with CVDs and healthy people by voiceprint using the MFCC and pitch

• Autorzy: Bourouhou Abdelhamid , Jilbab Abdelilah , Cherti Mohammed , Bourouhou Zaineb , Nacir Chafik

Identyfikatory

DOI

10.29354/diag/142560

• Języki publikacji: EN

Abstrakty

EN

Heart diseases cause many deaths around the world every year, and his death rate makes the leader of the killer diseases. But early diagnosis can be helpful to decrease those several deaths and save lives. To ensure good diagnose, people must pass a series of clinical examinations and analyses, which make the diagnostic operation expensive and not accessible for everyone. Speech analysis comes as a strong tool which can resolve the task and give back a new way to discriminate between healthy people and person with cardiovascular diseases. Our latest paper treated this task but using a dysphonia measurement to differentiate between people with cardiovascular disease and the healthy one, and we were able to reach 81.5% in prediction accuracy. This time we choose to change the method to increase the accuracy by extracting the voiceprint using 13 Mel-Frequency Cepstral Coefficients and the pitch, extracted from the people's voices provided from a database which contain 75 subjects (35 has cardiovascular diseases, 40 are healthy), three records of sustained vowels (aaaaa…, ooooo… .. and iiiiiiii….) has been collected from each one. We used the k-near-neighbor classifier to train a model and to classify the test entities. We were able to outperform the previous results, reaching 95.55% of prediction accuracy.

Słowa kluczowe

EN

cardiovascular diseases; speech analysis; voiceprint; MFCC; K-near-neighbor classifier

PL

choroby układu krążenia; analiza mowy; MFCC

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2021
• Tom: Vol. 22, No. 4
• Strony: 9--16
• Opis fizyczny: Bibliogr. 20 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

Cherti Mohammed

• University Mohammed V, Faculté de médecine et de pharmacie & CHU, Rabat, Morocco

autor

Bourouhou Zaineb

• University Mohammed V, Faculté de médecine et de pharmacie & CHU, Rabat, Morocco

autor

Nacir Chafik

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

Bibliografia

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• 6. Nabih-Ali M, El-Dahshan E-SA, Yahia AS. Heart diseases diagnosis using intelligent algorithm based on PCGsignal analysis. Circuits Syst. 2017; 8(7): 184-190.
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• 8. Carey MJ, Parris ES, Lloyd-Thomas H, Bennett S. Robust prosodic features for speaker identification. Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96, 1996;3: 1800-1803. https://doi.org/10.1109/ICSLP.1996.607979.
• 9. Jhanwar N, Raina AK. Pitch correlogram clustering for fast speaker identification. EURASIP J. Adv. Signal Process. 2004:37280. https://doi.org/10.1155/S1110865704408026.
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• 13. https://www.pngwave.com/png-clip-art-vijgd.
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• 15. Benba A, Jilbab A, Hammouch A. Voice analysis for detecting persons with Parkinson’s disease using MFCC and VQ. In The 2014 international conference on circuits, systems and signal processing, 23-25 September 2014. Saint Petersburg: Saint Petersburg State Polytechnic University 2014.
• 16. Young S, Evermann G, Hain T, Kershaw D, Liu X, Moore G, Odell J, Ollason D, Povey D, Valtchev V, Woodland P. The HTK book (for HTK version 3.4). Cambridge: Cambridge University Engineering Department. 2006.
• 17. 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.
• 18. 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.
• 19. 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.
• 20. Benba A, Jilbab A. Hammouch A. Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people. Int J Speech Technol. 2016;19:449-456. https://doi.org/10.1007/s10772-016-9338-4.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).