Automatic detection of Parkinson’s disease Using Zero-Time Window based cepstral feature

Parkinson’s disease (PD) is a neurological disorder that affects the nerves responsible for controlling movements. Automatic detection methods used for PD detection from the speech signal are important as they offer benefits such as reliability, repeatability, and cost-effectiveness. In this paper, we explored cepstral features derived from zero-time windowing (ZTW) based time-frequency analysis method for automatic detection of PD. This study used the PC-GITA database to evaluate the performance using support vector machine classifier and neural networks. The performance of the system is also compared with conventional mel-frequency cepstral coeffcients (MFCCs) features. Cepstral features derived from ZTW method shown better performance than baseline feature. The best performance of the PD detection system obtained using the cepstral features derived from ZTW method in terms of classification accuracy is 73.6%.