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Automated Detection of Wilson's Disease Based on Improved Mel-frequency Cepstral Coefficients with Signal Decomposition

Wilson’s disease (WD), a rare genetic movement disorder, is characterized by early-onset dysarthria. Automated speech assessment is thus valuable in early diagnosis and intervention. Time-frequency features, such as Mel-frequency cepstral coefficients (MFCC), have been frequently used. However, human speech signals are nonlinear and nonstationary, which cannot be captured by traditional features based on the Fourier transform. Moreover, the dysarthria type of WD patients is complex and different from other movement disorders such as Parkinson’s disease. Thus, developing sensitive time-frequency measures for WD patients is needed. The present study proposes DMFCC, the improved MFCC using signal decomposition. We validate the usefulness of DMFCC in WD detection with a sample of 60 WD patients and 60 matched healthy controls. Results show that the DMFCC achieves the best classification accuracy (86.1%), improving by 13.9%-44.4% compared to baseline features such as MFCC and the state-of-art Hilbert cepstral coefficients (HCCs). The present study is a first attempt to demonstrate the validity of automated acoustic measures in WD detection, and the proposed DMFCC provides a novel tool for speech assessment. feature patterns with potential neurobiologi-cal substrates using neuroimaging.