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Electronics
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Artificial Intelligence Methods for Biomedical Data Processing
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Artificial Intelligence Methods for Biomedical Data Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 2060

Special Issue Editor

Dr. Michael Winter

E-Mail Website
Guest Editor
Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany
Interests: data science; machine learning; artificial intelligence; digital technology; population data; process mining; mobile and wearable technologies

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest advancements in applying artificial intelligence (AI) techniques to the processing and analysis of biomedical data. Biomedical data science has experienced rapid growth in recent years, with AI playing a critical role in transforming raw data into actionable insights. This Special Issue is designed to showcase cutting-edge research that enhances AI's application in biomedical contexts while addressing the challenges and opportunities of processing complex, variable, and large volumes of data.

The focus of this Special Issue is on AI methodologies, including machine learning, deep learning, and natural language processing, that are specifically tailored to process biomedical data. These applications span genomics, medical imaging, patient health records, sensor data, and drug discovery. Additionally, the Special Issue will highlight innovative approaches that integrate AI into clinical practice, such as predictive analytics, disease detection, and personalized treatment plans. The scope covers both theoretical and practical aspects, ranging from novel algorithms and models to real-world applications and case studies. This includes implementing AI solutions in healthcare settings, and addressing ethical, regulatory, and technological challenges. The purpose is to foster interdisciplinary collaboration among AI researchers, biomedical scientists, and healthcare practitioners. By bridging the gap between AI research and real-world applications, this Special Issue seeks to inspire new research directions and enhance the translation of innovative AI solutions into clinical and biomedical environments.

In relation to existing literature, this Special Issue will serve to supplement the growing body of work on AI in biomedical data processing. While much of the current literature focuses on individual applications, such as AI in genomics or medical imaging, this Special Issue will provide a more comprehensive exploration of how AI can process diverse biomedical data types from genomic sequences to real-time patient data captured by wearable devices. Furthermore, by addressing the ethical implications, usability challenges, and regulatory issues of applying AI in these domains, it will offer a unique perspective that expands on the limitations of current studies. As a result, this Special Issue will provide a valuable resource for researchers and practitioners alike, contributing to the ongoing evolution of AI’s role in biomedical data processing.

We look forward to your contributions, which will help advance this critical field and inspire future innovations in AI-driven biomedical data solutions.

Dr. Michael Winter
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 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. Electronics 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 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

  • artificial intelligence in biomedicine
  • biomedical data processing
  • machine learning
  • deep learning
  • medical imaging and AI
  • genomic data analysis
  • predictive analytics in healthcare
  • natural language processing in medicine
  • data-driven healthcare solutions
  • AI-driven disease detection
  • personalized medicine through AI
  • ethics in AI for biomedical data
  • healthcare data privacy and AI
  • wearable devices and AI
  • digital health technologies

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

Published Papers (3 papers)

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22 pages, 1529 KiB  
Article
Exercise ECG Classification Based on Novel R-Peak Detection Using BILSTM-CNN and Multi-Feature Fusion Method
by Xinhua Su, Xuxuan Wang and Huanmin Ge
Electronics 2025, 14(2), 281; https://doi.org/10.3390/electronics14020281 - 12 Jan 2025
Viewed by 426
Abstract
Excessive exercise is a primary cause of sports injuries and sudden death. Therefore, it is vital to develop an effective monitoring technology for exercise intensity. Based on the noninvasiveness and real-time nature of an electrocardiogram (ECG), exercise ECG classification based on ECG features [...] Read more.
Excessive exercise is a primary cause of sports injuries and sudden death. Therefore, it is vital to develop an effective monitoring technology for exercise intensity. Based on the noninvasiveness and real-time nature of an electrocardiogram (ECG), exercise ECG classification based on ECG features could be used for detecting exercise intensity. However, current R-peak detection algorithms still have limitations, especially in high-intensity exercise scenarios and in the presence of noise interference. Additionally, the features utilized for exercise ECG classification are not comprehensive. To address these issues, the following tasks have been accomplished: (1) a hybrid time–frequency-domain model, BILSTM-CNN, is proposed for R-peak detection by utilizing BILSTM, multi-scale convolution, and an attention mechanism; (2) to enhance the robustness of the detector, a preprocessing data generator and a post-processing adaptive filter technique are proposed; (3) to improve the reliability of exercise intensity detection, the accurate heart rate variability (HRV) features derived from the proposed BILSTM-CNN and comprehensive features are constructed, which include various descriptive features (wavelets, local binary patterns (LBP), and higher-order statistics (HOS)) tested by the feasibility experiments and optimized deep learning features extracted from the continuous wavelet transform (CWT) of exercise ECG signals. The proposed system is evaluated by real ECG datasets, and it shows remarkable effectiveness in classifying five types of motion states, with an accuracy of 99.1%, a recall of 99.1%, and an F1 score of 99.1%. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Biomedical Data Processing)
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18 pages, 7539 KiB  
Article
MPB-UNet: Multi-Parallel Blocks UNet for MRI Automated Brain Tumor Segmentation
by Fatma Chahbar, Medjeded Merati and Saïd Mahmoudi
Electronics 2025, 14(1), 40; https://doi.org/10.3390/electronics14010040 - 26 Dec 2024
Viewed by 549
Abstract
Brain tumor segmentation in Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and treatment planning in neuro-oncology. This paper introduces a novel multi-parallel blocks UNet (MPB-UNet) architecture for automated brain tumor segmentation. Our approach enhances the standard UNet model by incorporating multiple [...] Read more.
Brain tumor segmentation in Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and treatment planning in neuro-oncology. This paper introduces a novel multi-parallel blocks UNet (MPB-UNet) architecture for automated brain tumor segmentation. Our approach enhances the standard UNet model by incorporating multiple parallel processing paths, inspired by the human visual system’s multi-scale processing capabilities. We integrate Atrous Spatial Pyramid Pooling (ASPP) to effectively capture multi-scale contextual information. We evaluated our proposed architecture using the publicly available Low-Grade Glioma (LGG) Segmentation Dataset. This comprehensive collection comprises 3929 axial slices of FLAIR MRI sequences from 110 patients, each slice paired with a corresponding segmentation mask. Our model demonstrated superior performances on this dataset compared with existing state-of-the-art methods, highlighting its effectiveness in accurate tumor delineation. We provide a comprehensive analysis of the model’s performance, including visual results and comparisons with other architectures. This work contributes to advancing automated brain tumor segmentation techniques, potentially improving diagnostic accuracy and efficiency in clinical settings. The proposed multi-parallel blocks UNet shows promise for integration into clinical workflows and opens avenues for future studies in medical image analysis. Our model achieves strong performances across multiple metrics: 99.86% accuracy, 99.86% precision, 99.86% sensitivity, 99.86% specificity, 99.80% Dice Similarity Coefficient (DSC), and 92.17% Average Intersection over Union (IoU). Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Biomedical Data Processing)
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20 pages, 1534 KiB  
Article
Machine-Learning-Based Validation of Microsoft Azure Kinect in Measuring Gait Profiles
by Claudia Ferraris, Gianluca Amprimo, Serena Cerfoglio, Giulia Masi, Luca Vismara and Veronica Cimolin
Electronics 2024, 13(23), 4739; https://doi.org/10.3390/electronics13234739 - 29 Nov 2024
Viewed by 790
Abstract
Gait is one of the most extensively studied motor tasks using motion capture systems, the gold standard for instrumental gait analysis. Various sensor-based solutions have been recently proposed to evaluate gait parameters, typically providing lower accuracy but greater flexibility. Validation procedures are crucial [...] Read more.
Gait is one of the most extensively studied motor tasks using motion capture systems, the gold standard for instrumental gait analysis. Various sensor-based solutions have been recently proposed to evaluate gait parameters, typically providing lower accuracy but greater flexibility. Validation procedures are crucial to assess the measurement accuracy of these solutions since residual errors may arise from environmental, methodological, or processing factors. This study aims to enhance validation by employing machine learning techniques to investigate the impact of such errors on the overall assessment of gait profiles. Two datasets of gait trials, collected from healthy and post-stroke subjects using a motion capture system and a 3D camera-based system, were considered. The estimated gait profiles include spatiotemporal, asymmetry, and body center of mass parameters to capture various normal and pathologic gait peculiarities. Machine learning models show the equivalence and the high level of agreement and concordance between the measurement systems in assessing gait profiles (accuracy: 98.7%). In addition, they demonstrate data interchangeability and integrability despite residual errors identified by traditional statistical metrics. These findings suggest that validation procedures can extend beyond strict measurement differences to comprehensively assess gait performance. Full article
(This article belongs to the Special Issue Artificial Intelligence Methods for Biomedical Data Processing)
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