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Electronics
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Edge AI for Biomedical Applications: Innovations in Sensing, Computing and...
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Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 2823

Special Issue Editors

Dr. Md Maruf Hossain Shuvo

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX 79912, USA
Interests: applied machine learning; deep learning; edge AI; circuits and devices for AI; biomedical signal and image analysis; biomedical instrumentation
Dr. Krishna Roy

E-Mail Website
Guest Editor
Department of Electrical Engineering, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
Interests: cybersecurity; human factors; autonomous systems security; AI security; audio visual safety; computer vision
Dr. Sanchita Ghose

E-Mail Website
Guest Editor
Department of Computer Engineering, San Francisco State University, San Francisco, CA 94132, USA
Interests: audio synthesis; Internet of Things; signal processing; deep learning; multi-modal and cross-modal analysis

Special Issue Information

Dear Colleagues,

The increasing prevalence of edge devices, such as wearables, cyber-physical systems, and the Internet of Things (IoT), in smart environments has catalyzed the integration of artificial intelligence (AI) directly into edge computing systems, a paradigm known as Edge AI. Edge AI marks a promising era for biomedical applications, enabling transformative innovations in sensing, computing, and security. This Special Issue emphasizes solutions that enable real-time, energy-efficient, and privacy-preserving machine learning and deep learning operations directly on edge devices, with applications including intelligent wearable sensors, implantable medical devices, clinical decision support systems, and mobile healthcare platforms.

The scope of the Special Issue spans a wide range of topics, including the following:

Advanced Sensing: The development and integration of novel biosensors and sensor fusion techniques that leverage Edge AI for precise data acquisition and analysis in real-time.

Efficient Computing: Algorithm–hardware co-design for energy-efficient AI inference, optimized machine learning models for edge devices, and low-power biomedical signal and image processing.

Robust Security: Ensuring data integrity and privacy in edge-based biomedical systems, with a focus on secure data transmission, federated learning, and adversarial robustness.

Applications: The deployment of Edge AI in diverse biomedical applications such as remote patient monitoring, wearable health diagnostics, neuroprosthetics, and personalized healthcare.

This Special Issue aims to bring together a collection of original research and review papers showcasing the unique constraints and opportunities of edge computing and AI in healthcare, such as latency-sensitive decision-making, resource-constrained environments, and enhanced data security, while addressing the gap between centralized AI methodologies and the emerging need for decentralized, edge-based systems tailored for biomedical applications.

Dr. Md Maruf Hossain Shuvo
Dr. Krishna Roy
Dr. Sanchita Ghose
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. 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

  • AI-enabled bioinstrumentation
  • biomedical edge computing
  • edge AI
  • embedded AI systems
  • cyber-physical systems
  • clinical decision support
  • efficient deep learning
  • real-time biosignal analytics
  • privacy and security at the edge
  • distributed intelligence

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Published Papers (4 papers)

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Research

21 pages, 8180 KiB  
Article
Resource-Constrained On-Chip AI Classifier for Beat-by-Beat Real-Time Arrhythmia Detection with an ECG Wearable System
by Mahfuzur Rahman and Bashir I. Morshed
Electronics 2025, 14(13), 2654; https://doi.org/10.3390/electronics14132654 - 30 Jun 2025
Viewed by 303
Abstract
The electrocardiogram (ECG) is one of the vital physiological signals for human health. Lightweight neural network (NN) models integrated into a low-resource wearable device can benefit the user with a low-power, real-time edge computing system for continuous and daily monitoring. This work introduces [...] Read more.
The electrocardiogram (ECG) is one of the vital physiological signals for human health. Lightweight neural network (NN) models integrated into a low-resource wearable device can benefit the user with a low-power, real-time edge computing system for continuous and daily monitoring. This work introduces a novel edge-computing wearable device for real-time beat-by-beat ECG arrhythmia classification. The proposed wearable integrates the light AI model into a 32-bit ARM® Cortex™-based custom printed circuit board (PCB). The work analyzes the performance of artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) models for real-time wearable implementation. The wearable is capable of real-time QRS detection and feature extraction from raw ECG data. The QRS detection algorithm offers high reliability with a 99.5% F1 score and R-peak position error (RPE) of 6.3 ms for R-peak-to-R-peak intervals. The proposed method implements a combination of top time series, spectral, and signal-specific features for model development. Lightweight, pretrained models are deployed on the custom wearable and evaluated in real time using mock data from the MIT-BIH dataset. We propose an LSTM model that provides efficient performance over accuracy, inference latency, and memory consumption. The proposed model offers 98.1% accuracy, with 98.2% sensitivity and 99.5% specificity while testing in real time on the wearable. Real-time inferencing takes 20 ms, and the device consumes as low as 5.9 mA of power. The proposed method achieves efficient performance in real-time testing, which indicates the wearable can be effectively used for real-time continuous arrhythmia detection. Full article
(This article belongs to the Special Issue Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security)
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23 pages, 1784 KiB  
Article
Signal-Specific and Signal-Independent Features for Real-Time Beat-by-Beat ECG Classification with AI for Cardiac Abnormality Detection
by I Hua Tsai and Bashir I. Morshed
Electronics 2025, 14(13), 2509; https://doi.org/10.3390/electronics14132509 - 20 Jun 2025
Viewed by 400
Abstract
ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone [...] Read more.
ECG monitoring is central to the early detection of cardiac abnormalities. We compared 28 manually selected signal-specific features with 159 automatically extracted signal-independent descriptors from the MIT BIH Arrhythmia Database. ANOVA reduced features to the 10 most informative attributes, which were evaluated alone and in combination with the signal-specific features using Random Forest, SVM, and deep neural networks (CNN, RNN, ANN, LSTM) under an interpatient 80/20 split. Merging the two feature groups delivered the best results: a 128-layer CNN achieved 100% accuracy. Power profiling revealed that deeper models improve accuracy at the cost of runtime, memory, and CPU load, underscoring the trade-off faced in edge deployments. The proposed hybrid feature strategy provides beat-by-beat classification with a reduction in the number of features, enabling real-time ECG screening on wearable and IoT devices. Full article
(This article belongs to the Special Issue Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security)
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20 pages, 4777 KiB  
Article
Quality Assurance of the Whole Slide Image Evaluation in Digital Pathology: State of the Art and Development Results
by Miklós Vincze, Béla Molnár and Miklós Kozlovszky
Electronics 2025, 14(10), 1943; https://doi.org/10.3390/electronics14101943 - 10 May 2025
Viewed by 532
Abstract
One of the key issues in medicine is quality assurance. It is essential to ensure the quality, consistency and validity of the various diagnostic processes performed. Today, the reproducibility and quality assurance of the analysis of digitized image data is an unsolved problem. [...] Read more.
One of the key issues in medicine is quality assurance. It is essential to ensure the quality, consistency and validity of the various diagnostic processes performed. Today, the reproducibility and quality assurance of the analysis of digitized image data is an unsolved problem. Our research has focused on the design and development of functionalities that can be used to greatly increase the verifiability of the evaluation of digitized medical image data, thereby reducing the number of misdiagnoses. In addition, our research presents a possible application of eye-tracking to determine the evaluation status of medical samples. At the beginning of our research, we looked at how eye-tracking technology is used in medical fields today and investigated the consistency of medical diagnoses. In our research, we designed and implemented a solution that can determine the evaluation state of a tomogram-type 3D sample by monitoring physiological and software parameters while using the software. In addition, our solution described in this paper is able to capture and reconstruct/replay complete VR diagnoses made in a 3D environment. This allows the diagnoses made in our system to be shared and further evaluated. We set up our own equations to quantify the evaluation status of a given 3D tomogram. At the end of the paper, we summarize our results and compare them with those of other researchers. Full article
(This article belongs to the Special Issue Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security)
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14 pages, 1605 KiB  
Article
A Supervised System Integrating Image Processing and Machine Learning for the Staging of Chronic Hepatic Diseases
by Giulia Iaconi, Alaa Wehbe, Paolo Borro, Marco Macciò and Silvana Dellepiane
Electronics 2025, 14(8), 1534; https://doi.org/10.3390/electronics14081534 - 10 Apr 2025
Viewed by 371
Abstract
Liver disease is a major global health concern. Given the critical role of medical image categorization in fibrosis staging (low, moderate, severe, cirrhotic) and the challenges posed by limited medical image datasets, this paper aims to leverage ultrasound imaging to assess liver margin [...] Read more.
Liver disease is a major global health concern. Given the critical role of medical image categorization in fibrosis staging (low, moderate, severe, cirrhotic) and the challenges posed by limited medical image datasets, this paper aims to leverage ultrasound imaging to assess liver margin characteristics at the level of Glisson’s capsule—here referred to as Glisson’s line—to develop a simple, automated model for accurately distinguishing fibrosis stages. The proposed approach combines traditional image processing techniques in a pre-processing stage with machine learning algorithms for classification. The pre-processing phase introduces an attention-focusing mechanism that stretches the gray levels of Glisson’s line while shrinking the intensity levels associated with the liver parenchyma and surrounding tissues. This results in the so-called region of contrast interest (ROCI), where potential classification distractors are minimized. For classification, a convolutional neural network (CNN)-based model is used to process original, rotated, and transformed ultrasound images. To address dataset imbalance and overfitting, a 10-fold cross-validation strategy was implemented. The results demonstrate that, by effectively enhancing the information content of Glisson’s line, different liver fibrosis stages can be accurately distinguished without the need for explicit edge detection, achieving accuracy levels comparable to those reported in the literature. The novelty of this work lies in analyzing the morphology of Glisson’s capsule—obtained through this method—rather than focusing on the liver parenchyma and texture, as is traditionally carried out. Full article
(This article belongs to the Special Issue Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security)
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