CNN AND LSTM FOR THE CLASSIFICATION OF PARKINSON'S DISEASE BASED ON THE GTCC AND MFCC
Nouhaila BOUALOULOU
nouhailaboualoulou21@gmail.comLaboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). (Morocco)
Taoufiq BELHOUSSINE DRISSI
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (Morocco)
https://orcid.org/0000-0003-2958-070X
Benayad NSIRI
Research Center STIS, M2CS, National Higher School of Arts and Craft, Rabat (ENSAM) (Morocco)
https://orcid.org/0000-0003-3885-9534
Abstract
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. In this paper, we present a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics 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.
Keywords:
Parkinson's disease; voice signal; GTCC, MFCC; DWT; EMD; CNN and LSTM.References
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Authors
Nouhaila BOUALOULOUnouhailaboualoulou21@gmail.com
Laboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics (GEITIIL). Morocco
Authors
Taoufiq BELHOUSSINE DRISSILaboratory Electrical and Industrial Engineering, Information Processing, Informatics, and Logistics Morocco
https://orcid.org/0000-0003-2958-070X
Authors
Benayad NSIRIResearch Center STIS, M2CS, National Higher School of Arts and Craft, Rabat (ENSAM) Morocco
https://orcid.org/0000-0003-3885-9534
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