Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 01 | Page :
    By

    Bibhu Prasad Ganthia,

  • Rosalin Pradhan,

  1. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
  2. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India

Abstract

This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The framework employs a multi-resolution decomposition strategy combined with adaptive filtering to enhance signal clarity and robustness under varying noise conditions. Experimental validation on electrocardiogram (ECG) signals and wireless communication datasets demonstrates superior performance in terms of signal-to-noise ratio improvement, reconstruction accuracy, and computational efficiency compared to conventional techniques such as wavelet-based and empirical mode decomposition methods. Moreover, the suggested framework demonstrates improved adaptability to various signal environments, rendering it appropriate for managing highly nonlinear and complex datasets. By integrating fractal and entropy features, computational redundancy is reduced, which allows for faster convergence and enhances the capability of real-time implementation. The proposed model shows strong potential for real-time applications, including medical diagnostics and reliable data transmission in dynamic environments. Overall, the FEG-ASR framework provides a scalable and intelligent solution for next-generation signal processing challenges.

Keywords: Fractal entropy, adaptive signal reconstruction, non-stationary signals, biomedical signal processing, ECG analysis, communication systems, noise reduction, multi-resolution analysis, signal decomposition, intelligent filtering.

How to cite this article:
Bibhu Prasad Ganthia, Rosalin Pradhan. Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems. Current Trends in Signal Processing. 2026; 17(01):-.
How to cite this URL:
Bibhu Prasad Ganthia, Rosalin Pradhan. Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems. Current Trends in Signal Processing. 2026; 17(01):-. Available from: https://journals.stmjournals.com/ctsp/article=2026/view=239651


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Ahead of Print Subscription Review Article
Volume 17
01
Received 31/03/2026
Accepted 31/03/2026
Published 03/04/2026
Publication Time 3 Days
18

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