Detection of Parkinson’s Disease from gait using Neighborhood Representation Local Binary Patterns

Abstract

Parkinson's Disease (PD) is a neurodegenerative disease that affects millions of people around the world. Diagnostics tools based on the clinical symptoms have been developed by the scientific community mostly in the last decade. This study proposes a new method of PD detection from gait signals, using artificial neural networks and a novel technique framework called Neighborhood Representation Local Binary Pattern (NR-LBP). Vertical Ground Reaction Force (VGRF) readings are preprocessed and transformed using several methods within the proposed framework. Statistical features are extracted from the transformed data, and the Student's t-test test is used to create different feature sets. A simple artificial neural network is trained over these features to detect PD, and its performance is evaluated using different metrics. Classification accuracy of 98.3% and Matthews Correlation Coefficient of 0.959 are obtained, indicating high-performance classification. Based on these performance measures, the proposed NR-LBP algorithm is compared to the regular LBP algorithm and found to be contributing positively to classification performance when various types of transformations are used in combination.