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Biomedicines
Special Issues
Recent Advances in Biomedical Engineering for the Elderly
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Recent Advances in Biomedical Engineering for the Elderly

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 944

Editor

Dr. Siti Anom Ahmad

E-Mail Website
Guest Editor
Control System and Signal Processing Research Centre, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Serdang 43400, Selangor, Malaysia
Interests: biomedical engineering; artificial intelligence system; gerontechnology; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With ageing populations becoming a global reality, biomedical engineering is playing a pivotal role in reshaping elderly care. This Special Issue of Biomedicines focuses on recent advances that integrate technology, clinical knowledge, and user-centred design to improve health outcomes and daily living for older adults. It brings together emerging research that demonstrates how biomedical innovations, from wearable sensors and intelligent health systems to assistive robotics and remote monitoring, can empower ageing individuals to live safer, healthier, and more autonomous lives.

Aim and Scope

This Special Issue aims to showcase the latest research and technological developments in biomedical engineering that support the health, independence, and quality of life of the ageing population. As the global demographic shifts toward an increasingly elderly population, the need for innovative, accessible, and effective biomedical solutions has never been more urgent.

We invite original research articles, reviews, and short communications that explore the design, development, and evaluation of biomedical systems, devices, and technologies tailored to the elderly. Topics of interest include, but are not limited to the following:

  • Assistive and rehabilitative devices;
  • Wearable sensors for health monitoring;
  • AI-driven health analytics and diagnostics;
  • Physiological signal processing for ageing-related conditions;
  • Human–machine interaction and usability studies for the elderly;
  • Biomedical interventions for age-related diseases (e.g., cardiovascular, neurological, and musculoskeletal).

This Special Issue seeks interdisciplinary contributions from biomedical engineers, clinicians, AI researchers, gerontologists, and healthcare innovators that address real-world challenges in elderly care using advanced biomedical engineering approaches.

Dr. Siti Anom Ahmad
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines 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 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

  • biomedical engineering
  • gerontechnology
  • artificial intelligence in healthcare
  • digital health
  • physiological signal processing
  • human–machine interaction
  • rehabilitation engineering

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Published Papers (1 paper)

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Research

23 pages, 659 KB  
Article
EEG-ChTABNet: A Dual-Branch Channel-Wise Transformer with Gated Attention-Branch Network for EEG-Based Classification of Dementia
by Noor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali and Siti Anom Ahmad
Biomedicines 2026, 14(6), 1345; https://doi.org/10.3390/biomedicines14061345 - 15 Jun 2026
Viewed by 409
Abstract
Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep [...] Read more.
Background/Objectives: Early and accurate discrimination of neurological conditions, dementia, stroke and healthy aging, remains a critical clinical challenge. Electroencephalography (EEG) is a non-invasive measure of brain dynamics and entropy-based features obtained from multichannel EEG have shown strong discriminative ability. However, existing deep learning approaches do not sufficiently address the combined challenges of small clinical cohorts and high-dimensional entropy feature spaces. In this study, a novel architecture is proposed for multi-class neurological EEG classification under extreme small-sample conditions. Methods: A novel dual-branch Channel-wise Transformer and Attention-Branch Network (EEG-ChTABNet) are pr to classify 19-channel EEG entropy features into three classes (dementia, stroke, healthy control; N = 45; 15 per class). The architecture suggests four new designs. First, the Channel Importance Attention (CIA) block, which adaptively learns to re-weight the importance of electrodes via squeeze-excitation. Second, the dual-branch encoder, which combines the global multi-head self-attention with the local depthwise-separable convolution. Third, the gated sigmoid fusion mechanism. Fourth, the bottleneck residual classification head, to solve overfitting. Eight entropy feature sets: Amplitude-Aware Permutation Entropy (AAPE), Attention Entropy (AttEn), Dispersion Entropy (DisEn), Distribution Entropy (DistrEn), Fluctuation-based Dispersion Entropy (FDispEn), Fuzzy Entropy (FuzEn), Linear Gaussian Estimation of the Conditional Entropy (LinEn), and Symbolic Dynamics (SyDy) were evaluated individually with stratified 5-fold cross-validation on within-fold SMOTE augmentation. Results: EEG-ChTABNet consistently outperformed the baseline Transformer on all 8 feature sets. DisEn and SyDy features yielded peak classification accuracy of 73.3% (AUC: 0.823 and 0.857, respectively) compared to the corresponding baseline of 57.8% and 55.6%. SyDy achieved the best overall AUC of 0.857 and the dementia detection sensitivity was up to 86.7% over multiple feature sets. Conclusions: EEG-ChTABNet shows the effectiveness of channel-adaptive, dual-branch Transformer Designs for EEG-based neurological classification from Small-Sample Entropy Feature Data, and Identifying SyDy and DisEn as the Most Discriminative Feature Representations for Three-Class Neurological EEG Classification. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Engineering for the Elderly)
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