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AI-Based Biomedical Signal Processing—2nd Edition
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AI-Based Biomedical Signal Processing—2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 10808

Special Issue Editors

Dr. Agnese Sbrollini

E-Mail Website
Guest Editor
Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Interests: bioengineering; cognitive computation; biostatistics; cardiac signals processing; sport; pregnancy; cardiology
Special Issues, Collections and Topics in MDPI journals
Dr. Aurora Saibene

E-Mail Website
Guest Editor
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milano, Italy
Interests: brain computer interfacing; electrophysiological signal processing, analysis and classification; motor movement and imagery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is inspiring new solutions to challenges typically present in the healthcare field. AI-based innovative tools have revealed their effectiveness in different steps of the biomedical signal processing blockchain, including biomedical signal (i) acquisition, (ii) preprocessing, (iii) feature engineering and (iv) classification/interpretation. AI-based methods may find solutions to biomedical signal processing challenges by integrating sensors and acquisition systems. Moreover, they can represent new approaches to preprocess, characterize, classify, and interpret biomedical signals. These solutions may be essential in all fields of healthcare, including cardiology, neurology, endocrinology, movement analysis, physical activity monitoring, assistive robotics, telemedicine, and others.

This Special Issue aims to collect original research papers and/or reviews on AI-based methods for biomedical signal processing.

Main topics include, but are not limited to, the following:

  • Intelligent sensors, devices and instruments for biomedical signal acquisition;
  • AI-based biomedical signal preprocessing;
  • Machine learning for biomedical feature extraction and selection;
  • Knowledge engineering for feature interpretation;
  • AI-based clinical decision making in healthcare;
  • AI-based precision medicine;
  • Data analytics and mining for clinical decision support;
  • Ethics of AI in healthcare.

Dr. Agnese Sbrollini
Dr. Aurora Saibene
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 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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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
  • biomedical signal processing
  • filtering and denoising
  • machine and deep learning
  • clinical decision-support systems
  • cognitive computing
  • computer vision
  • interpretability

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Related Special Issue

Published Papers (7 papers)

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16 pages, 2379 KB  
Article
An Integrated 60 GHz Radar and AI-Guided Infrared System for Non-Contact Heart Rate and Body Temperature Monitoring
by Sangwook Sim and Changgyun Kim
Appl. Sci. 2026, 16(7), 3272; https://doi.org/10.3390/app16073272 - 27 Mar 2026
Viewed by 439
Abstract
The growing need for remote patient monitoring, accelerated by the global pandemic and an aging population, necessitates the development of advanced non-contact technologies for measuring vital signs. In this study, an integrated, non-contact system for accurately measuring heart rate (HR) and body temperature [...] Read more.
The growing need for remote patient monitoring, accelerated by the global pandemic and an aging population, necessitates the development of advanced non-contact technologies for measuring vital signs. In this study, an integrated, non-contact system for accurately measuring heart rate (HR) and body temperature (BT) is developed and validated. The proposed system combines a 60 GHz radar sensor and infrared (IR) sensor for HR and BT measurements, respectively, enhanced with advanced signal processing and an AI-based computer vision algorithm. A Window Filter and a Peak Uniformity algorithm were applied to the raw radar signal to mitigate noise and motion artifacts. For Temp measurement, an IR sensor with a narrow five-degree field of view (FOV) was integrated with a YOLO Pose-based tracking system using a camera and servo motors to automatically orient the sensor towards the user’s face. The system was validated with 30 healthy adult participants, benchmarked against a MAX30102 PPG sensor and Braun ThermoScan 7 for BT and BT measurements, respectively. The advanced signal processing reduced the HR Mean Absolute Error from 13.73 BPM to 5.28 BPM (p = 0.002), while the AI-guided IR sensor reduced the BT MAE from 4.10 °C to 1.64 °C (p < 0.001). These findings demonstrate that integrating 60 GHz radar with AI-driven tracking provides a promising approach for home-based trend monitoring. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing—2nd Edition)
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17 pages, 1870 KB  
Article
Non-Invasive Blood Glucose Monitoring via Multimodal Features Fusion with Interpretable Machine Learning
by Ying Shan and Junsheng Yu
Appl. Sci. 2026, 16(2), 790; https://doi.org/10.3390/app16020790 - 13 Jan 2026
Cited by 1 | Viewed by 1055
Abstract
This study aimed to develop a non-invasive blood glucose estimation method by integrating wearable multimodal signals, including photoplethysmography (PPG), electrodermal activity (EDA), and skin temperature (ST), with food log–derived nutritional features, and to validate its clinical reliability. We analyzed data from 16 adults [...] Read more.
This study aimed to develop a non-invasive blood glucose estimation method by integrating wearable multimodal signals, including photoplethysmography (PPG), electrodermal activity (EDA), and skin temperature (ST), with food log–derived nutritional features, and to validate its clinical reliability. We analyzed data from 16 adults who underwent continuous glucose monitoring (CGM) while multimodal physiological signals were collected over 8–10 consecutive days, yielding more over 20,000 paired samples. Features from food logs and physiological signals were extracted, followed by feature selection using Boruta and minimum Redundancy Maximum Relevance (mRMR). Five machine learning models were trained and evaluated using five-fold cross-validation. Food log features alone demonstrated stronger predictive power than unimodal physiological signals. The fusion of nutritional, physiological, and temporal features achieved the best accuracy using LightGBM, reducing the RMSE to 12.9 mg/dL, with a MARD of 7.9%, a MAE of 8.82 mg/dL, and R2 of 0.69. SHapley Additive exPlanations (SHAP) analysis revealed that 24-h carbohydrate and sugar intake, time since last meal, and short-term EDA features were the most influential predictors. By integrating multimodal wearable and dietary information, the proposed framework significantly enhances non-invasive glucose estimation. The interpretable LightGBM model demonstrates promising clinical utility for continuous monitoring and early dysglycemia management. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing—2nd Edition)
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20 pages, 2451 KB  
Article
Toward Embedded Multi-Level Classification of 12-Lead ECG Signal Quality Using Spectrograms and CNNs
by Francisco David Pérez Reynoso, Jorge Alberto Soto Cajiga, Luis Alberto Gordillo Roblero and Paola Andrea Niño Suárez
Appl. Sci. 2025, 15(24), 12976; https://doi.org/10.3390/app152412976 - 9 Dec 2025
Viewed by 1195
Abstract
This study presents an open and replicable methodology for multi-lead ECG signal quality assessment (SQA), implemented on a 12-lead embedded acquisition platform. Signal quality is a critical software component for diagnostic reliability and compliance with international standards such as IEC 60601-2-27 (clinical ECG [...] Read more.
This study presents an open and replicable methodology for multi-lead ECG signal quality assessment (SQA), implemented on a 12-lead embedded acquisition platform. Signal quality is a critical software component for diagnostic reliability and compliance with international standards such as IEC 60601-2-27 (clinical ECG monitors), IEC 60601-2-47 (ambulatory ECG systems), and IEC 62304 (software life cycle for medical devices) which define the essential engineering requirements and functional performance for medical devices. Unlike proprietary SQA algorithms embedded in closed commercial systems such as Philips DXL™, the proposed method provides a transparent and auditable framework that enables independent validation and supports adaptation for research and clinical prototyping. Our approach combines convolutional neural networks (CNNs) with FFT-derived spectrograms to perform four-level signal quality classification (High, Medium, Low, and Unidentifiable), achieving up to 95.67% accuracy on the test set, confirming the robustness of the CNN-based spectrogram classification model. The algorithm has been validated on a custom controlled dataset generated using the Fluke PS420™ hardware simulator, enabling controlled replication of signal artifacts for software-level evaluation. Designed for execution on resource-constrained embedded platforms, the system integrates real-time preprocessing and wireless transmission, demonstrating its feasibility for deployment in mobile or decentralized ECG monitoring solutions. These results establish a software validation proof-of-concept that goes beyond algorithmic performance, addressing regulatory expectations such as those outlined in FDA’s Good Machine Learning Practice (GMLP). While clinical validation remains pending, this work contributes a standards-aligned methodology to democratize advanced SQA functionality and support future regulatory-compliant development of embedded ECG system. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing—2nd Edition)
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25 pages, 6767 KB  
Article
A Sequential Segmentation and Classification Learning Approach for Skin Lesion Images
by Mirco Gallazzi, Ignazio Gallo and Silvia Corchs
Appl. Sci. 2025, 15(23), 12614; https://doi.org/10.3390/app152312614 - 28 Nov 2025
Viewed by 981
Abstract
This study investigates how the learning order between segmentation and classification tasks influences performance and generalization in medical image analysis. We propose a Sequential Swin Transformer framework that reuses a shared Transformer backbone with alternating task-specific heads to compare two sequential strategies: (i) [...] Read more.
This study investigates how the learning order between segmentation and classification tasks influences performance and generalization in medical image analysis. We propose a Sequential Swin Transformer framework that reuses a shared Transformer backbone with alternating task-specific heads to compare two sequential strategies: (i) segmentation followed by classification and (ii) classification followed by segmentation. Unlike conventional multitask or preprocessing-based pipelines, the proposed framework isolates the impact of task ordering on feature transfer under an identical architecture. Evaluated on the HAM10000 skin lesion dataset, the segmentation-then-classification configuration achieves the highest multiclass accuracy (up to 86.9%) while maintaining strong segmentation performance (Jaccard index ≈ 86%). Statistical tests confirm its superiority in accuracy and macro F1 score, whereas Grad-CAM and t-distributed stochastic neighbor embedding (t-SNE) analyses reveal that segmentation-first training yields more lesion-centered attention and a more discriminative latent space. Cross-domain evaluation on gastrointestinal endoscopy images further demonstrates robust segmentation (Jaccard index ≈ 91%) and multiclass accuracy (≈94.5%), confirming the generalizability of the sequential paradigm. Overall, the proposed method provides a theoretically grounded, clinically interpretable, and reproducible alternative to joint multitask learning approaches, enhancing feature transfer and generalization in medical imaging. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing—2nd Edition)
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17 pages, 1910 KB  
Article
Automated Signal Quality Assessment for rPPG: A Pulse-by-Pulse Scoring Method Designed Using Human Labelling
by Lieke Dorine van Putten, Aristide Jun Wen Mathieu and Simon Wegerif
Appl. Sci. 2025, 15(20), 10915; https://doi.org/10.3390/app152010915 - 11 Oct 2025
Viewed by 1113
Abstract
Reliable analysis of remote photoplethysmography (rPPG) signals depends on identifying physiologically plausible pulses. Traditional approaches rely on clustering self-similar pulses, which can discard valid variability. Automating pulse quality assessment could capture the true underlying morphology while preserving physiological variability. In this manuscript, individual [...] Read more.
Reliable analysis of remote photoplethysmography (rPPG) signals depends on identifying physiologically plausible pulses. Traditional approaches rely on clustering self-similar pulses, which can discard valid variability. Automating pulse quality assessment could capture the true underlying morphology while preserving physiological variability. In this manuscript, individual rPPG pulses were manually labelled as plausible, borderline and implausible and used to train multilayer perceptron classifiers. Two independent datasets were used to ensure strict separation between training and test data: the Vision-MD dataset (4036 facial videos from 1270 participants) and a clinical laboratory dataset (235 videos from 58 participants). Vision-MD data were used for model development with an 80/20 training–validation split and 5-fold cross-validation, while the clinical dataset served exclusively as an independent test set. A three-class model was evaluated achieving F1-scores of 0.92, 0.24 and 0.79 respectively. Recall was highest for plausible and implausible pulses but lower for borderline pulses. To test separability, three pairwise binary classifiers were trained, with ROC-AUC > 0.89 for all three category pairs. When combining borderline and implausible pulses into a single class, the binary classifier achieved an F1-score of 0.93 for the plausible category. Finally, usability analysis showed that automated labelling identified more usable pulses per signal than the previously used agglomerative clustering method, while preserving physiological variability. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing—2nd Edition)
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20 pages, 1206 KB  
Article
Multilayer Neural-Network-Based EEG Analysis for the Detection of Epilepsy, Migraine, and Schizophrenia
by İbrahim Dursun, Mehmet Akın, M. Ufuk Aluçlu and Betül Uyar
Appl. Sci. 2025, 15(16), 8983; https://doi.org/10.3390/app15168983 - 14 Aug 2025
Cited by 2 | Viewed by 1901
Abstract
The early detection of neurological and psychiatric disorders is critical for optimizing patient outcomes and improving the efficacy of healthcare delivery. This study presents a novel multiclass machine learning (ML) framework designed to classify epilepsy, migraine, and schizophrenia simultaneously using electroencephalography (EEG) signals. [...] Read more.
The early detection of neurological and psychiatric disorders is critical for optimizing patient outcomes and improving the efficacy of healthcare delivery. This study presents a novel multiclass machine learning (ML) framework designed to classify epilepsy, migraine, and schizophrenia simultaneously using electroencephalography (EEG) signals. Unlike conventional approaches that predominantly rely on binary classification (e.g., healthy vs. diseased cohorts), this work addresses a significant gap in the literature by introducing a unified artificial neural network (ANN) architecture capable of discriminating among three distinct neurological and psychiatric conditions. The proposed methodology involves decomposing raw EEG signals into constituent frequency subbands to facilitate robust feature extraction. These discriminative features were subsequently classified using a multilayer ANN, achieving performance metrics of 95% sensitivity, 96% specificity, and a 95% F1-score. To enhance clinical applicability, the model was optimized for potential integration into real-time diagnostic systems, thereby supporting the development of a rapid, reliable, and scalable decision support tool. The results underscore the viability of EEG-based multiclass models as a promising diagnostic aid for neurological and psychiatric disorders. By consolidating the detection of multiple conditions within a single computational framework, this approach offers a scalable and efficient alternative to traditional binary classification paradigms. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing—2nd Edition)
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22 pages, 975 KB  
Systematic Review
Machine Learning to Recognise ACL Tears: A Systematic Review
by Julius Michael Wolfgart, Ulf Krister Hofmann, Maximilian Praster, Marina Danalache, Filippo Migliorini and Martina Feierabend
Appl. Sci. 2025, 15(9), 4636; https://doi.org/10.3390/app15094636 - 22 Apr 2025
Cited by 2 | Viewed by 3236
Abstract
Machine learning-based tools are becoming increasingly popular in clinical practice. They offer new possibilities but are also limited in their reliability and accuracy. The present systematic review updates and discusses the existing literature regarding machine learning algorithm-based identification of cruciate ligament injury on [...] Read more.
Machine learning-based tools are becoming increasingly popular in clinical practice. They offer new possibilities but are also limited in their reliability and accuracy. The present systematic review updates and discusses the existing literature regarding machine learning algorithm-based identification of cruciate ligament injury on radiographic images. PubMed was searched for articles containing machine learning algorithms related to cruciate ligament injury recognition. No additional filters or time constraints were used. All eligible studies were accessed by hand. From the 115 articles initially retrieved, 29 articles were finally included. Only one study included the posterior cruciate ligament (PCL). Deep learning algorithms in the form of convolutional neural networks (CNNs) were most frequently used. Many studies presented CNNs that identified binary decision classes of regular and torn anterior cruciate ligaments (ACLs) with a best sensitivity of 0.98, a specificity of 0.99, and an AUC ROC of 1.0. Other studies expanded the decision classes to partially torn ACLs or reconstructed ACLs, usually at the cost of sensitivity and specificity. Deep learning algorithms are excellent for identifying ACL injuries, tears, or postoperative status after reconstruction on MRI images. They are much faster but only sometimes better than the human reviewer. While the technology seems ready, barriers to ethical and legal issues and clinicians’ refusals must be overcome to some extent. It can be firmly assumed that artificial intelligence will have a future contribution in the diagnosis of cruciate ligament injuries. Full article
(This article belongs to the Special Issue AI-Based Biomedical Signal Processing—2nd Edition)
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