A novel Parkinson's disease detection algorithm combined EMD, BFCC, and SVM classifier

• Autorzy: Boualoulou Nouhaila , Mounia Miyara , Nsiri Benayad , Behoussine Drissi Taoufiq

Identyfikatory

DOI

10.29354/diag/171712

• Języki publikacji: EN

Abstrakty

EN

Identifying and assessing Parkinson's disease in its early stages is critical to effectively monitoring the disease's progression. Methodologies based on machine learning enhanced speech analysis are gaining popularity as the potential of this field is revealed. Acoustic features, in particular, are used in a variety of algorithms for machine learning and could serve as indicators of the general health of subjects' voices. In this research paper, a novel method is introduced for the automated detection of Parkinson's disease through speech signal analysis, a support vector machines classifier (SVM) and an Artificial Neural Network (ANN) are used to evaluate and classify the data based on two acoustic features: Bark Frequency Cepstral Coefficients (BFCC) and Mel Frequency Cepstral Coefficients (MFCC). These features are extracted from the denoised signals using Empirical Mode Decomposition (EMD). The most relevant results obtained for a dataset of 38 participants are by the BFCC coefficients with an accuracy up to 92.10%. These results confirm that EMD-BFCC-SVM method can contribute to the detection of Parkinson's disease.

Słowa kluczowe

EN

EMD; BFCC; MFCC; SVM; Parkinson’s disease

PL

SVM; BFCC; MFCC; sztuczna sieć neuronowa; choroba Parkinsona

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 4
• Strony: art. no. 2023404
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Boualoulou Nouhaila

• Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco
• Research Center STIS, M2CS, National Higher School of Arts and Craft, Rabat (ENSAM). Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0001-8565-8239

autor

Mounia Miyara

• Computer Science and Systems Laboratory (LIS), Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco

autor

Nsiri Benayad

• Research Center STIS, M2CS, National Higher School of Arts and Craft, Rabat (ENSAM). Mohammed V University in Rabat, Morocco

• https://orcid.org/0000-0003-38859534

autor

Behoussine Drissi Taoufiq

• Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco

• https://orcid.org/0000-0003-2958-070X

Bibliografia

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