Detection of abnormal movement in Parkinson's disease using time irreversibility and entropy

Abstract

This work aims to determine how entropy and time irreversibility can help differentiate between healthy and parkinsonian movement. Acceleration signals from control subjects and patients with Parkinson's disease (PD) were analyzed using statistical tools, entropy, and time irreversibility. Results were compared with signals from three toy systems: oscillatory, random, and complex signals. Entropy values were higher in control subjects compared to Parkinson's disease patients, indicating less ordered behavior in healthy movement. Time irreversibility was found to be lower in control subjects, indicating a less dissipative system. This study provides useful insights into the differences between healthy and parkinsonian movements. Our approach shows potential to distinguish between control and PD patients in clinical practice using acceleration signals, offering a potential diagnostic tool to evaluate movement disorders such as Parkinson's disease.