CNN and LSTM for the classification of parkinson's disease based on the GTCC and MFCC

• Autorzy: Boualoulou Nouhaila , Drissi Taoufiq Belhoussine , Nsiri Benayad

Identyfikatory

DOI

10.35784/acs-2023-11

• Języki publikacji: EN

Abstrakty

EN

Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 percent of Parkinson's disease sufferers have some form of early speech impairment, recent studies on tele diagnosis of Parkinson's disease have focused on the recognition of voice impairments from vowel phonations or the subjects' discourse. This paper presents a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics. These are Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) obtained from noise-removed speech signals with comparative EMD-DWT and DWT-EMD analysis. The proposed model is divided into three stages. In the first step, noise is removed from the signals using the EMD-DWT and DWT-EMD methods. In the second step, the GTCC and MFCC are extracted from the enhanced audio signals. The classification process is carried out in the third step by feeding these features into the LSTM and CNN models, which are designed to define sequential information from the extracted features. The experiments are performed using PC-GITA and Sakar datasets and 10-fold cross validation method, the highest classification accuracy for the Sakar dataset reached 100% for both EMD-DWT-GTCC-CNN and DWT-EMD-GTCC-CNN, and for the PC-GITA dataset, the accuracy is reached 100% for EMD-DWT-GTCC-CNN and 96.55% for DWT-EMD-GTCC-CNN. The results of this study indicate that the characteristics of GTCC are more appropriate and accurate for the assessment of PD than MFCC.

Słowa kluczowe

EN

Parkinson's disease; voice signal; GTCC; MFCC; DWT; EMD; CNN and LSTM

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2023
• Tom: Vol. 19, no 2
• Strony: 1--24
• Opis fizyczny: Bibliogr.35 poz., fig., 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

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

autor

Drissi Taoufiq Belhoussine

• 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

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/0000-0003-3885-9534

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