- Tytuł:
- A novel Parkinsons disease detection algorithm combined EMD, BFCC, and SVM classifier
- Autorzy:
-
Boualoulou, Nouhaila
Mounia, Miyara
Nsiri, Benayad
Behoussine Drissi, Taoufiq - Powiązania:
- https://bibliotekanauki.pl/articles/27313826.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
- Tematy:
-
EMD
BFCC
MFCC
SVM
Parkinson’s disease
sztuczna sieć neuronowa
choroba Parkinsona - Opis:
- 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.
- Źródło:
-
Diagnostyka; 2023, 24, 4; art. no. 2023404
1641-6414
2449-5220 - Pojawia się w:
- Diagnostyka
- Dostawca treści:
- Biblioteka Nauki
Artykuł