Signal and Visual Approaches for Parkinson's Disease Detection from Spiral Drawings

Abstract

The development of medical decision-support technologies that provide accurate biomarkers to physicians is an important research area. For example, in the case of Parkinson's Disease (PD), the current supervisions of patients become intrusive, occasional, and subjective. However, new technologies such as wearable devices, signal processing, computer vision, and deep learning could offer a non-intrusive, continuous, and objective solution to help physicians with patient monitoring. The Parkinson's Disease Spiral Drawings public dataset was selected to face PD detection in this work by comparing four representation methods of the X, Y, and the pressure time series: signal, visual, hand-crafted, and fusion. The signal approach uses the Fast Fourier Transform of recording windows and a Convolutional Neural Network for modeling; the visual strategy employs visual transformer features from gray-scale images; the hand-crafted technique utilizes statistics calculated from temporal signals, and the fusion combines the information from the previous approaches. In these procedures, a Random Forest classifier was used for PD detection using the attributes extracted from each type of representation. The best results showed an F1 score of 93.33% and 93.06% at the user level using a signal approach with the three signals for the Static Spiral Task and an image-based proposal with X and Y coordinates for the Dynamic Spiral Task, respectively.