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Mathematics
Special Issues
Mathematical Methods for Signal Analysis
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Mathematical Methods for Signal Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1437

Special Issue Editors

Dr. Miloš Milovanović

E-Mail Website
Guest Editor
Mathematical Institute of the Serbian Academy of Sciences and Arts, Kneza Mihaila 36, 11000 Belgrade, Serbia
Interests: mathematical physics; complex systems; time operator formalism; neuroaesthetics
Dr. Nicoletta Saulig

E-Mail Website
Guest Editor
Faulty of Engineering, Juraj Dobrila University of Pula, 52100 Pula, Croatia
Interests: signal processing; detection; statistical signal processing; frequency analysis; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mathematical modeling has emerged as a cornerstone of modern life sciences, providing powerful tools to decode complex biological processes and uncover their underlying principles. This Special Issue invites innovative contributions exploring mathematical and computational approaches across diverse areas, including the following:

  • Biomedical Signal Processing: Advances in interpreting physiological signals, such as ECG, EEG, and EMG, for improved diagnostics and treatment.
  • Genomic Signal Processing: Mathematical analysis of genetic sequences and gene expression data to unlock insights into biological function and regulation.
  • Imaging and Optical Signal Processing: Models that enhance diagnostic imaging and drive breakthroughs in microscopy and medical optics.
  • Sensory and Biological Visual Systems: Approaches to understand early-stage processing in vision, mechanisms of neural encoding, and perceptual representation in sensory pathways.
  • Psychophysical Analysis of Visual Perception: Quantitative methods to link sensory inputs with perceptual and cognitive experiences.

By fostering cross-disciplinary research, this Special Issue aims to advance our understanding of sensory systems, neural mechanisms, and biological processes. Authors are encouraged to submit their original work that applies mathematical and computational techniques to unravel the complexities of these systems and support transformative applications in life sciences.

Dr. Miloš Milovanović
Dr. Nicoletta Saulig
Guest Editors

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • mathematical modeling in life sciences
  • biomedical signal processing
  • genomic signal processing
  • imaging and optical signal processing
  • sensory systems
  • biological visual systems
  • psychophysical analysis of sensory perception

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Published Papers (1 paper)

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Research

30 pages, 972 KB  
Article
A Unified Framework for Detection of ADHD Using EEG Signals and Coherent Models
by Sunil Kumar Prabhakar and Dong-Ok Won
Mathematics 2026, 14(5), 871; https://doi.org/10.3390/math14050871 - 4 Mar 2026
Viewed by 382
Abstract
A behavioral and neuropsychological disorder that develops in young children during their early school years is called attention-deficit hyperactivity disorder (ADHD). When young children are diagnosed with ADHD, they have a tendency not to concentrate on academic and extracurricular activities. Moreover, children affected [...] Read more.
A behavioral and neuropsychological disorder that develops in young children during their early school years is called attention-deficit hyperactivity disorder (ADHD). When young children are diagnosed with ADHD, they have a tendency not to concentrate on academic and extracurricular activities. Moreover, children affected with ADHD suffer from mood swings, so it becomes quite difficult for them to establish good connections with teachers and friends. In the field of clinical research, deploying Electroencephalography (EEG) signals, a rapid and accurate diagnosis of ADHD is essential so that an effective treatment can be given to the children affected with ADHD. In this work, a unified framework is proposed for the detection of ADHD using EEG signals and some coherent models. The framework initially employs the concept of normalization of EEG signals, followed by the usage of dimensionality reduction techniques such as Local Linear Embedding (LLE), Sammon Mapping (SM) and Locally Linear Coordination (LLC). The dimensionally reduced EEG values are further clustered using four techniques such as spectral clustering, K-means clustering, Fuzzy C-means (FCM) clustering, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and finally, silhouette coefficient analysis is used to analyze the clustering effectiveness. The features are then extracted from the clustered values using an Improved Wavelet Transform (IWT) and then the features are selected with four efficient techniques such as the chi-squared test, Mutual Information (MI), Mahalanobis analysis and Binary Horse Herd Optimization (BHHO) techniques. Finally, the selected values are fed into classifiers for classification with the help of ten traditional machine learning classifiers. The work is tested on a publicly available ADHD dataset and the analysis shows that the best results are obtained when the LLC dimensionality reduction is utilized with FCM clustering and IWT feature extraction, BHHO feature selection, and classified with LGBA classifier reporting a high classification accuracy of 98.12%. Full article
(This article belongs to the Special Issue Mathematical Methods for Signal Analysis)
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