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Bioengineering
Special Issues
Intelligent IoMT Systems for Brain–Computer Interface
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Intelligent IoMT Systems for Brain–Computer Interface

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3562

Special Issue Editors

Dr. Saadullah Farooq Abbasi

E-Mail Website
Guest Editor
Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Interests: cyber security; deep learning; biomedical engineering; sleep classification; MedSecurance
Dr. Faisal Jamil

E-Mail Website
Guest Editor
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
Interests: Internet of Things; machine learning; WBAN; blockchain; microgrid; indoor localization; task orchestration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The evolution of modern medicine and healthcare hinges on the integration of automation, facilitating precise and prompt medical interventions crucial for optimal patient care. The burgeoning landscape of intelligent service systems within healthcare and medicine offers a pathway toward fast, accurate, and reliable diagnoses, prevention strategies, and treatments. This Special Issue endeavors to curate a compendium of cutting-edge, data-driven solutions that form the bedrock of smart systems in healthcare and medicine.

Central to this Special Issue is the intersection of the intelligent Internet of Medical Things (IoMT) and brain–computer interface (BCI) systems, pioneering a realm where real-time solutions meet the nuances of neurological healthcare using biosignal processing. Emphasizing the synergy between advanced technology and medical care, this Special Issue invites contributions elucidating the seamless integration of intelligent IoMT devices with BCI, focusing on their role in augmenting brain disease detection, diagnosis precision, biosignal classification, and patient-centric care.

Dr. Saadullah Farooq Abbasi
Dr. Faisal Jamil
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. Bioengineering 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

  • Internet of Medical Things
  • brain computer interface
  • artificial intelligence
  • machine learning: electroencephalography
  • electrocardiogram

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Published Papers (2 papers)

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Research

21 pages, 4723 KiB  
Article
A Comparative Study on Imputation Techniques: Introducing a Transformer Model for Robust and Efficient Handling of Missing EEG Amplitude Data
by Murad Ali Khan
Bioengineering 2024, 11(8), 740; https://doi.org/10.3390/bioengineering11080740 - 23 Jul 2024
Cited by 1 | Viewed by 1532
Abstract
In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional [...] Read more.
In clinical datasets, missing data often occur due to various reasons including non-response, data corruption, and errors in data collection or processing. Such missing values can lead to biased statistical analyses, reduced statistical power, and potentially misleading findings, making effective imputation critical. Traditional imputation methods, such as Zero Imputation, Mean Imputation, and k-Nearest Neighbors (KNN) Imputation, attempt to address these gaps. However, these methods often fall short of accurately capturing the underlying data complexity, leading to oversimplified assumptions and errors in prediction. This study introduces a novel Imputation model employing transformer-based architectures to address these challenges. Notably, the model distinguishes between complete EEG signal amplitude data and incomplete data in two datasets: PhysioNet and CHB-MIT. By training exclusively on complete amplitude data, the TabTransformer accurately learns and predicts missing values, capturing intricate patterns and relationships inherent in EEG amplitude data. Evaluation using various error metrics and R2 score demonstrates significant enhancements over traditional methods such as Zero, Mean, and KNN imputation. The Proposed Model achieves impressive R2 scores of 0.993 for PhysioNet and 0.97 for CHB-MIT, highlighting its efficacy in handling complex clinical data patterns and improving dataset integrity. This underscores the transformative potential of transformer models in advancing the utility and reliability of clinical datasets. Full article
(This article belongs to the Special Issue Intelligent IoMT Systems for Brain–Computer Interface)
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22 pages, 2382 KiB  
Article
Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities
by Hina Ayub, Murad-Ali Khan, Syed Shehryar Ali Naqvi, Muhammad Faseeh, Jungsuk Kim, Asif Mehmood and Young-Jin Kim
Bioengineering 2024, 11(6), 533; https://doi.org/10.3390/bioengineering11060533 - 23 May 2024
Cited by 1 | Viewed by 1501
Abstract
The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing [...] Read more.
The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities. Full article
(This article belongs to the Special Issue Intelligent IoMT Systems for Brain–Computer Interface)
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