An Intelligent Approach based on the Combination of the Discrete Wavelet Transform, Delta Delta MFCC for Parkinson's Disease Diagnosis

To diagnose Parkinson's disease (PD), it is necessary to monitor the progression of symptoms. Unfortunately, diagnosis is often confirmed years after the onset of the disease. Communication problems are often the first symptoms that appear earlier in people with Parkinson's disease. In this study, we focus on the signal of speech to discriminate between people with and without PD, for this, we used a Spanish database that contains 50 records of which 28 are patients with Parkinson's disease and 22 are healthy people, these records contain five types of supported vowels (/a/, /e/, /i/, /o/ and /u/), The proposed treatment is based on the decomposition of each sample using Discrete Wavelet Transform (DWT) by testing several kinds of wavelets, then extracting the delta delta Mel Frequency Cepstral Coefficients (delta delta MFCC) from the decomposed signals, finally we apply the decision tree as a classifier, the purpose of this process is to determine which is the appropriate wavelet analyzer for each type of vowel to diagnose Parkinson's disease.