Detection of early Parkinson’s disease with wavelet features using finger typing movements on a keyboard

This study presents a new Parkinson’s disease diagnosis technique based on wavelets extracted features and machine learning paradigms. The present-day diagnosis techniques suffer from low diagnosis accuracy and also require the patient to go to a medical facility, where the diagnosis is done by a specialist. In this work, we propose an automatic diagnosis method where by, all the patient has to do is to type some keys on their keyboard, and the algorithm will calculate the latency time, flight time and hold time of each key pressed, to make a diagnosis of Parkinson’s disease. We use several wavelets to extract some features that are classified into Parkinson’s disease or non-Parkinson’s disease. The results are very encouraging and we obtain a classification accuracy of up to 100% in some of the cases, using a ten-fold cross-validation technique. Wavelets are a tool that can be used to complement and improve the detection of Parkinson’s disease. These results will permit the amelioration of some state-of-the-art methods which use a similar technique to detect Parkinson’s disease.