Multiclass classification of Parkinson’s disease using cepstral analysis
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This paper addressees the problem of an early diagnosis of PD (Parkinson’s disease) by the classification of characteristic features of person’s voice knowing that 90% of the people with PD suffer from speech disorders. We collected 375 voice samples from healthy and people suffer from PD. We extracted from each voice sample features using the MFCC and PLP Cepstral techniques. All the features are analyzed and selected by feature selection algorithms to classify the subjects in 4 classes according to UPDRS (unified Parkinson’s disease Rating Scale) score. The advantage of our approach is the resulting and the simplicity of the technique used, so it could also extended for other voice pathologies. We used as classifier the discriminant analysis for the results obtained in previous multiclass classification works. We obtained accuracy up to 87.6% for discrimination between PD patients in 3 different stages and healthy control using MFCC along with the LLBFS algorithm.
KeywordsParkinson’s disease Cepstral analysis Classification PCA LLBFS Discriminant analysis
These Datasets were generated through collaboration between Sage Bionetworks, PatientsLikeMe and Dr. Max Little as part of the Patient Voice Analysis study (PVA). They were obtained through Synapse ID [syn2321745].
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