A novel Parkinson's disease detection algorithm combined EMD, BFCC, and SVM classifier
1
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco.
2
Computer Science and Systems Laboratory (LIS), Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco
3
Research Center STIS, M2CS, National Higher School of Arts and Craft, Rabat (ENSAM). Mohammed V University in Rabat, Morocco
Submission date: 2023-06-02
Final revision date: 2023-07-27
Acceptance date: 2023-08-31
Online publication date: 2023-10-09
Publication date: 2023-10-09
Corresponding author
Nouhaila Boualoulou
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco.
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco.
Diagnostyka 2023;24(4):2023403
KEYWORDS
TOPICS
ABSTRACT
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.
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