Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction

Harun Yaşar Köse, Serhat İkizoğlu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

Original languageEnglish
Article number1385
JournalEntropy
Volume25
Issue number10
DOIs
Publication statusPublished - Oct 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Funding

This research constitutes a significant component of a project entitled “Development of an Algorithm for Dynamic Vestibular System Analysis and Design of a Balance Detector,” which received funding from the Scientific and Technological Research Council of Türkiye (TÜBİTAK) for conducting the experiments (Project no: 115E258).

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu115E258

    Keywords

    • classification
    • detrending
    • feature extraction
    • gait analysis
    • insole force sensors
    • Tsallis entropy
    • vestibular disorders

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