Diagnosis of Parkinson's disease from electroencephalography signals using linear and self-similarity features

An early stage detection of Parkinson's disease (PD) is crucial for its appropriate treatment. The quality of life degrades with the advancement of the disease. In this paper, we propose a natural (time) domain technique for the diagnosis of PD. The proposed technique eliminates the need for transformation of the signal to other domains by extracting the feature of electroencephalography signals in the time domain. We hypothesize that two inter-channel similarity features, correlation coefficients and linear predictive coefficients, are able to detect the PD signals automatically using support vector machines classifier with third degree polynomial kernel. A progressive feature addition analysis is employed using selected features obtained based on the feature ranking and principal component analysis techniques. The proposed approach is able to achieve a maximum accuracy of 99.1 +/- 0.1%. The presented computer-aided diagnosis system can act as an assistive tool to confirm the finding of PD by the clinicians.