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Fractal Fract
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Fractals in Physiology and Medicine
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Fractals in Physiology and Medicine

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Life Science, Biophysics".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 3364

Special Issue Editor

Prof. Dr. Igor V. Pantic

E-Mail Website
Guest Editor
Faculty of Medicine, Institute for Medical Physiology, University of Belgrade, 11000 Belgrade, Serbia
Interests: computational methods in medicine; fractal analysis in medicine; fractional biological dynamics and biophysics; fractals in neurosciences; fractal analysis for artificial intelligence

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the role of fractal geometry and fractal analysis in understanding complex biological systems and medical phenomena, as well as their potential applications in contemporary medical physiology research. This Special Issue will address the application of fractal analysis to physiological signals, including both one-dimensional time series (e.g., ECG, EEG) and two-dimensional biomedical images (e.g., microscopy images or radiograms). It will also examine how fractal-based features may be used in the development of machine learning models for physiological research, thus enhancing the classification, prediction, and interpretation of biological patterns. This collection also aims to compile knowledge encompassing both mathematics and biomedical engineering, and contribute to a systems-level understanding of medical physiology and related fields.

Prof. Dr. Igor V. Pantic
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fractal and Fractional is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fractal analysis
  • physiological signals
  • biomedical imaging
  • machine learning in physiology
  • complex systems
  • computational physiology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

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24 pages, 2611 KB  
Article
MF-DFA–Enhanced Deep Learning for Robust Sleep Disorder Classification from EEG Signals
by Abdulaziz Alorf
Fractal Fract. 2026, 10(3), 199; https://doi.org/10.3390/fractalfract10030199 - 18 Mar 2026
Viewed by 593
Abstract
Sleep disorders are prevalent in the world, and they lead to severe health issues such as cardiovascular disease and cognitive disabilities. Conventional polysomnography-based diagnosis is based on manual EEG analysis under the supervision of trained specialists, which is time-consuming and may have inter-rater [...] Read more.
Sleep disorders are prevalent in the world, and they lead to severe health issues such as cardiovascular disease and cognitive disabilities. Conventional polysomnography-based diagnosis is based on manual EEG analysis under the supervision of trained specialists, which is time-consuming and may have inter-rater variability. Although the predictions of deep learning (DL) models on the task of sleep classification of EEG have been promising, they, in many cases, do not explain the multiscale, temporal dynamics that physiological signals are characterized by. In this work, a hybrid model that is a combination of CNN and multifractal detrended fluctuation analysis (MF-DFA) was proposed to detect localized temporal features and long-term fractal-based dynamics of single-channel EEG recordings. The performance of the suggested model was tested using two separate polysomnographic datasets: the CAP Sleep Dataset of five-class sleep disorder classification (Healthy, Insomnia, Narcolepsy, PLM, and RBD) and the ISRUC Sleep Dataset on the three-class subject-independent validation. In the CAP dataset, the framework had an accuracy of 86.38%. Cross-dataset transfer to the ISRUC Sleep Dataset, where only the classification head was fine-tuned on a small labeled subset while all feature-extraction layers remained frozen from CAP training, achieved 87.50% accuracy, demonstrating that the learned representations generalize across differing recording protocols, sampling rates, and diagnostic label spaces. The experiments of ablation proved the paramount importance of the MF-DFA features, and the lack of them led to low classification rates. The findings demonstrate the clinical feasibility of applying fractal analysis in conjunction with DL to detect sleep disorders in an automated, generalizable manner, suitable for use in large-scale monitoring and resource-starved clinical environments. Full article
(This article belongs to the Special Issue Fractals in Physiology and Medicine)
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Review

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16 pages, 1050 KB  
Review
Fractal Analysis of Auditory Evoked Potentials: Research Gaps and Potential AI Applications
by Svetlana Valjarevic, Jovana Paunovic Pantic, Jelena Cumic, Peter R. Corridon and Igor Pantic
Fractal Fract. 2026, 10(1), 20; https://doi.org/10.3390/fractalfract10010020 - 29 Dec 2025
Cited by 3 | Viewed by 2175
Abstract
Auditory evoked potentials (AEPs) are electroencephalographic (EEG) responses to auditory stimuli and are frequently used to evaluate auditory processing and cognitive integrity. Interpretation of AEPs today predominantly relies on standard linear techniques such as time-domain averaging and frequency-domain spectral decomposition. These approaches may [...] Read more.
Auditory evoked potentials (AEPs) are electroencephalographic (EEG) responses to auditory stimuli and are frequently used to evaluate auditory processing and cognitive integrity. Interpretation of AEPs today predominantly relies on standard linear techniques such as time-domain averaging and frequency-domain spectral decomposition. These approaches may not always capture nonlinear, nonstationary, and scale-free characteristics of EEG signals; therefore, in contemporary neurophysiology research, there may be a need for the utilization of additional nonlinear frameworks. Fractal analysis may be a powerful tool for the quantification of subtle changes in EEG and AEP complexity, irregularity, and variability. This approach is often overlooked due to methodological and conceptual limitations but nevertheless holds significant potential in revealing alterations in geometrical and spatial complexity of AEPs under various physiological conditions. Here, we discuss potential applications and shortcomings of fractal AEP analysis, as well as its possible integration with supervised machine learning algorithms. We also focus on novel artificial intelligence-based concepts that could, in theory, utilize the power of fractal AEP and EEG analysis to improve the classification and prediction of neurophysiological processes and phenomena. Full article
(This article belongs to the Special Issue Fractals in Physiology and Medicine)
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