Abstract

Over millions of people are suffering from Parkinson's disease (PD), which is one of the most common neuro-degenerative disease. Unfortunately, this disease is just hard to predict. One way to identify PD is by listening to the patient's voice, since they experience several vocal degradations. As a result, voice data is effective for diagnosing disease. Some of the patients who are affected and suffering from PD, it has been observed that handwriting impacts of patients is straight forwardly proportional to the seriousness of PD. Those affected by Parkinson's disease tends to write slower and use less pressure while sketching or writing something. A better clinical diagnosis will be possible with the accurate and precise identification of such biomarkers at the onset of the disease. The purpose of this paper is to present a system design for analyzing voice impairments, spiral drawings and wave drawings in persons those are affected by Parkinson's disease (PD) as well as healthy subjects. In this research paper, two different CNN are used for analyzing patterns of both spiral and wave drawings respectively and a system using artificial neural network for analyzing voice impairments is also implemented. The complete model will be trained on the parkinsons disease voice and pattern data.