A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform

Abstract In recent years, there has been increasing interest in the development of telediagnosis and telemonitoring systems for Parkinson’s disease (PD) based on measuring the motor system disorders caused by the disease. As approximately 90% percent of PD patients exhibit some form of vocal disorders in the earlier stages of the disease, the recent PD telediagnosis studies focus on the detection of the vocal impairments from sustained vowel phonations or running speech of the subjects. In these studies, various speech signal processing algorithms have been used to extract clinically useful information for PD assessment, and the calculated features were fed to learning algorithms to construct reliable decision support systems. In this study, we apply, to the best of our knowledge for the first time, the tunable Q-factor wavelet transform (TQWT) to the voice signals of PD patients for feature extraction, which has higher frequency resolution than the classical discrete wavelet transform. We compare the effectiveness of TQWT with the state-of-the-art feature extraction methods used in diagnosis of PD from vocal disorders. For this purpose, we have collected the voice recordings of 252 subjects in the context of this study and extracted multiple feature subsets from the voice recordings. The feature subsets are fed to multiple classifiers and the predictions of the classifiers are combined with ensemble learning approaches. The results show that TQWT performs better or comparable to the state-of-the-art speech signal processing techniques used in PD classification. We also find that Mel-frequency cepstral and the tunable-Q wavelet coefficients, which give the highest accuracies, contain complementary information in PD classification problem resulting in an improved system when combined using a filter feature selection technique.