Spectral Electroencephalographic and Heart Rate Variability features enhance identification of medicated/non-medicated Parkinson’s disease patients

Parkinson’s Disease is a neuropathy that produces changes in several biomarkers, these changes could be used to evaluate even sub–clinical conditions. This paper presents an evaluation of indices extracted from electroencephalography and Heart Rate Variability (HRV), when used to classify a sample of subjects from three groups: control (healthy), medicated and non medicated subjects diagnosed with Parkinson’s disease. Classification performance was measured using accuracy over these classes and a cross validation scheme was used to assess repeatability for the classification process. Results tend to prove that inclusion of an autonomic index derived from HRV analysis enhances classification, suggesting that Parkinson’s disease could be related with unperceptible to mild alterations of the Autonomic Nervous System.