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Processes
Special Issues
Design, Fabrication, Modeling, and Control in Biomedical Systems
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Design, Fabrication, Modeling, and Control in Biomedical Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 949

Special Issue Editors

Prof. Dr. Fulufhelo Nemavhola

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Faculty of Engineering, and the Built Environment, Durban University of Technology, Durban 4000, South Africa
Interests: membrane
Prof. Dr. Thanyani Pandelani

E-Mail Website
Guest Editor
Department of Mechanical, Bioresources and Biomedical Engineering, Collge of Science Engineering and Technology, University of South Africa, Pretoria, South Africa
Interests: characterisation of soft biological tissues using various methods; magnetic resonance imaging (MRI) of soft tissue; mechanical testing of soft tissue using biaxial testing machine; trauma biomechanics
Dr. Harry Ngwangwa

E-Mail Website
Guest Editor
Department of Mechanical, Bioresources and Biomedical Engineering, Collge of Science Engineering and Technology, University of South Africa, Pretoria, South Africa
Interests: condition monitoring of engineering structures; rotordynamics; machinery and infrastructure health monitoring using AI methods; biomimetic designing

Special Issue Information

Dear Colleagues,

The rapid advancement of engineering and computational techniques has significantly transformed biomedical systems, leading to innovative solutions for diagnostics, therapy, rehabilitation, and patient care. This Special Issue, titled "Design, Fabrication, Modeling, and Control in Biomedical Systems", aims to bring together state-of-the-art research in the multidisciplinary domain of biomedical engineering, covering fundamental principles, novel methodologies, and emerging applications.

Biomedical systems are complex and integrate mechanical, electronic, and computational components to enhance healthcare outcomes. Their success relies on the synergy between design innovation, fabrication techniques, modeling accuracy, and precise control. This Special Issue seeks to explore novel methods and applications that push the boundaries of biomedical technology, offering new insights into the future of medical devices and intelligent healthcare systems.

We invite original research articles, comprehensive reviews, and case studies addressing, but not limited to, the following topics:

  • Design and development of biomedical systems;
  • Advanced fabrication techniques;
  • Modeling and simulation;
  • Control and optimization in biomedical systems;
  • Emerging applications and future directions.

We look forward to your submissions.

Prof. Dr. Fulufhelo Nemavhola
Prof. Dr. Thanyani Pandelani
Dr. Harry Ngwangwa
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Processes 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 2400 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

  • biomedical systems
  • medical device design
  • additive manufacturing in healthcare
  • computational modeling in biomedicine
  • control systems in biomedical applications
  • AI-driven biomedical engineering
  • finite-element analysis in healthcare
  • biocompatible materials
  • wearable and implantable devices
  • robotics in healthcare
  • digital twins in medicine
  • smart materials for medical applications
  • nanotechnology in biomedicine
  • cyber-physical systems in healthcare
  • internet of medical things (IoMT)
  • human–machine interaction in medicine

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

Published Papers (1 paper)

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Research

26 pages, 3103 KB  
Article
An Interpretable Model for Cardiac Arrhythmia Classification Using 1D CNN-GRU with Attention Mechanism
by Waleed Ali, Talal A. A. Abdullah, Mohd Soperi Zahid, Adel A. Ahmed and Hakim Abdulrab
Processes 2025, 13(8), 2600; https://doi.org/10.3390/pr13082600 - 17 Aug 2025
Viewed by 675
Abstract
Accurate classification of cardiac arrhythmias remains a crucial task in biomedical signal processing. This study proposes a hybrid deep learning approach called 1D CNN-eGRU that integrates one-dimensional convolutional neural network models (1D CNN) and a gated recurrent unit (GRU) architecture with an attention [...] Read more.
Accurate classification of cardiac arrhythmias remains a crucial task in biomedical signal processing. This study proposes a hybrid deep learning approach called 1D CNN-eGRU that integrates one-dimensional convolutional neural network models (1D CNN) and a gated recurrent unit (GRU) architecture with an attention mechanism for the precise classification of cardiac arrhythmias based on ECG Lead II signals. To enhance the classification of cardiac arrhythmias, we also address data imbalances in the MIT-BIH arrhythmia dataset by introducing a hybrid data balancing method that blends resampling and class-weight learning. Additionally, we apply Sig-LIME, a refined variant of LIME tailored for signal datasets, to provide comprehensive insights into model decisions. The suggested hybrid 1D CNN-eGRU approach, a fusion of 1D CNN-GRU along with an attention mechanism, is designed to acquire intricate temporal and spatial dependencies in ECG signals. It aims to distinguish between four distinct arrhythmia classes from the MIT-BIH dataset, addressing a significant challenge in medical diagnostics. Demonstrating strong performance, the proposed hybrid 1D CNN-eGRU model achieves an overall accuracy of 0.99, sensitivity of 0.93, and specificity of 0.99. Per-class evaluation shows precision ranging from 0.80 to 1.00, sensitivity from 0.83 to 0.99, and F1-scores between 0.82 and 0.99 across four arrhythmia types (normal, supraventricular, ventricular, and fusion). The model also attains an AUC of 1.00 on average, with a final test loss of 0.07. These results not only demonstrate the model’s effectiveness in arrhythmia classification but also underscore the added value of interpretability enabled through the use of the Sig-LIME technique. Full article
(This article belongs to the Special Issue Design, Fabrication, Modeling, and Control in Biomedical Systems)
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