Selection of entropy based features for the analysis of the Archimedes' spiral applied to essential tremor

TL;DR

This study evaluates entropy-based features derived from Archimedes' spiral drawings to improve the automatic diagnosis of essential tremor using machine learning techniques.

Contribution

It introduces a method for selecting entropy features from spiral drawings for better essential tremor diagnosis, integrating multiple entropy algorithms and machine learning.

Findings

Entropy features effectively distinguish ET from healthy controls

Multiple entropy algorithms provide complementary information

Machine learning models achieve high diagnostic accuracy

Abstract

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.