Some Non-Obvious Consequences of Non-Extensiveness of Entropy

Wilk, G.; Włodarczyk, Z.

doi:10.3390/e25030474

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…In (2), N is the number of possible states and p i represents the probability of the i-th state. The determination of the value of the parameter q does not have specific criteria, but rather depends on the specific characteristics of the analyzed dataset [26]. By adjusting the value of q, the entropy metric can be tailored to capture particular features inherent in the analyzed dataset.…”

Section: Entropy Tsallis Entropy-brief Backgroundmentioning

confidence: 99%

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

Köse,

İkizoğlu

2023

Entropy

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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.

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Section: Entropy Tsallis Entropy-brief Backgroundmentioning

confidence: 99%

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

Köse,

İkizoğlu

2023

Entropy

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show abstract

“…The approach based on Equation (1) has been applied in different scenarios such as relativistic gas [20], Bose-Einstein condensation [21,22], seismology [23], and high energy multiparticle production [24]. Simultaneously with these developments, fractional calculus has been advanced in different fields as a powerful approach to incorporating different aspects of the system with extensions of the differential operators to a noninteger order.…”

Section: Introductionmentioning

confidence: 99%

Aspects of Quantum Statistical Mechanics: Fractional and Tsallis Approaches

2023

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We investigated two different approaches, which can be used to extend the standard quantum statistical mechanics. One is based on fractional calculus, and the other considers the extension of the concept of entropy, i.e., the Tsallis statistics. We reviewed and discussed some of the main properties of these approaches and used the thermal Green function formalism to perform the developments, simultaneously allowing us to analyze each case’s dynamics and thermodynamics aspects. In particular, the results allow us to understand how the extensions change the behavior of some quantities, particularly fluctuations related to the system.

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Nonextensive Entropy Application On Detrended Force Sensor Data to Feature Balance Disorder of Patients with Vestibular System Dysfunction

Köse,

İkizoğlu

2023

Preprint

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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 to reduce the data acquisition period significantly. The extracted feature set is then inputted into fundamental classification algorithms, with the Support-Vector-Machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step of a large project aiming to identify specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

show abstract

Some Non-Obvious Consequences of Non-Extensiveness of Entropy

Cited by 3 publications

References 48 publications

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

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

Aspects of Quantum Statistical Mechanics: Fractional and Tsallis Approaches

Nonextensive Entropy Application On Detrended Force Sensor Data to Feature Balance Disorder of Patients with Vestibular System Dysfunction

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