Selection of entropy based features for the analysis of the Archimedes' spiral applied to essential tremor
This work addresses the diagnosis of essential tremor, a common movement disorder, but appears incremental as it applies existing entropy algorithms and machine learning methods to a specific biomedical dataset.
The researchers tackled the problem of diagnosing essential tremor by analyzing Archimedes' spiral drawings using entropy-based features and machine learning, aiming to identify nonlinear biomarkers for improved clinical assessment.
Biomedical systems are regulated by interacting mechanisms that operate across multiple spatial and temporal scales and produce biosignals with linear and non-linear information inside. In this sense entropy could provide a useful measure about disorder in the system, lack of information in time-series and/or irregularity of the signals. Essential tremor (ET) is the most common movement disorder, being 20 times more common than Parkinson's disease, and 50-70% of this disease cases are estimated to be genetic in origin. Archimedes spiral drawing is one of the most used standard tests for clinical diagnosis. This work, on selection of nonlinear biomarkers from drawings and handwriting, is part of a wide-ranging cross study for the diagnosis of essential tremor in BioDonostia Health Institute. Several entropy algorithms are used to generate nonlinear feayures. The automatic analysis system consists of several Machine Learning paradigms.