Automatic Detection of Parkinson’s Disease using Cepstral Features derived from Speech Signals

Abstract

A Parkinson’s Detection System refers to a set of tools designed to assist in the early diagnosis of Parkinson’s Disease, a progressive neuro-degenerative disease that affects movement control. In literature about Parkinson’s Disease (PD) detection systems, various speech analysis tools were used which included features derived from the voice harmonics, jitter and shimmer, speech rate, etc. While these features describe the changes in the voice patterns of the patient, we found that statistical data of the cepstral coefficients are a more effective classification criteria. This resulted in our use of cepstral coefficients derived from two different methods in our system and the results are compared to a baseline model which uses MFCCs. The first method is focused on the transformation of the speech signal through the time frequency treatment of a wavelet transform to make use of the multi resolution property of the wavelet transform. It is followed by a cepstral analysis in order to extract the cepstral coefficients. The resultant feature dimensions are classified using Support Vector Machine (SVM) with the help of different kernels. This model has been applied to PC-GITA database of diadokinetic word /pa-ta-ka/. The results showed us a performance of 65%. The second method is the Zero-Time Windowing of the speech signal and it allows us a higher spectro-temporal resolution. The cepstral coefficients derived from this signal are used as as the basis for training the model for detection of Parkinson’s Disease. The dataset and classifiers are same as the first method and resulted in 70% when the derivatives of the cepstral features are also used. Both the proposed systems compared favourably to the baseline used which is MFCC based detection system.