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A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

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Multimedia Tools and Applications 2025年第8期84卷 5349-5375页

作者： Shafique, Arslan Mehmood, Abid Alawida, Moatsum Elhadef, Mourad Rehman, Mujeeb Ur School of Biomedical Engineering University of Glasgow Glasgow United Kingdom Department of Computer Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Cyber Technology Institute School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Within the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications. © The Author(s) 2024.

关键词： Cryptography

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Supervised learning applied to electrocardiogram statistical features for the detection of premature ventricular contraction

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Research on biomedical engineering 2025年第1期41卷 1-12页

作者： Issa, Khouloud Rammal, Abbas Assaf, Rabih Ghandour, Ahmad Electrical and Communication Engineering Department College of Engineering Phoenicia University Zahrani South Lebanon Statistics and Informatics Department Faculty of Science Lebanese University Beirut Lebanon Mathematics and Computer Sciences Department Faculty of Arts and Sciences Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Arts and Sciences Holy Spirit University of Kaslik Jounieh Lebanon Biomedical Engineering Department Faculty of Engineering Islamic University of Lebanon Wardenieh30014 Lebanon

Purpose: The development of an automated premature ventricular contraction (PVC) detection system has significant implications for early intervention and treatment decisions. This study aims to develop a novel approach using various supervised machine learning (ML) methods for detecting PVCs in electrocardiogram (ECG) recordings. Methodology: To achieve this, we extracted ten distinct statistical and temporal features from 33 long-term ECG recordings from the benchmark MIT-BIH arrhythmia database (MIT-BIH-AD), capturing significant characteristics from the signals. We then investigated the effectiveness of traditional ML algorithms in identifying PVCs based on these features, namely support vector machine (SVM), decision tree (DT), naïve Bayes (NB), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and artificial neural network (ANN). Results: Among these classifiers, the SVM, KNN, ANN, and DT classifiers demonstrated exceptional discriminatory power and classification performance, yielding near-perfect area under the receiver operating characteristic curve (AUC-ROC) values of 97.3%, 95.4%, 95%, and 94.1%, respectively. On the other hand, the SVM classifier emerged as the most accurate, achieving an overall accuracy of 97.39%, indicative of making correct predictions. Conclusion: These findings underscore the generalization of the proposed approach, especially since we worked on an extended dataset encompassing different types of heartbeats to discriminate PVCs from other heartbeat types, not only normal ones, and providing closer alignment with real-world scenarios. Subsequently, the robustness and accuracy of these models highlight their suitability for clinical applications and their potential for efficient implementation and deployment in the diagnosis of cardiovascular diseases (CVDs), as they demand minimal computational resources and time. © The Author(s), under exclusive licence to The Brazilian Society of biomedical engineering 2025.

关键词： Decision trees

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MHFNet: A Multimodal Hybrid-embedding Fusion Network for Automatic Sleep Staging

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IEEE Journal of biomedical and Health informatics 2025年第5期29卷 3387-3397页

作者： Liu, Ruhan Li, Jiajia Wen, Yang Huang, Xian Sheng, Bin Feng, David Dagan Zhang, Ping Central South University Furong Laboratory Hunan Changsha China Xiangya Hospital Central South University Department of Dermatology Hunan Changsha China Shanghai Jiao Tong University Shanghai School of Chemistry and Chemical Engineering National Center for Translational Medicine China College of Electronics and Information Engineering Shenzhen China The Third Xiangya Hospital of Central South University Department of Neurology China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China The University of Sydney School of Computer Science SydneyNSW2006 Australia Ohio State University Department of Computer Science and Engineering ColumbusOH43210 United States Ohio State University Department of Biomedical Informatics ColumbusOH43210 United States

Scoring sleep stages is essential for evaluating the status of sleep continuity and comprehending its structure. Despite previous attempts, automating sleep scoring remains challenging. First, most existing works did not fuse local and global temporal information. Second, the correlation for special waves in different signals is rarely used in sleep staging modeling. Third, the logic of scoring rules based on adjacent epochs is not considered in developing sleep staging models. This paper introduces a multimodal hybrid-embedding fusion network (MHFNet), which aims to tackle these challenges in automating sleep stage scoring. MHFNet comprises multi-stream Xception blocks to extract wave characteristics, a hybrid time-embedding module to combine local and global temporal information, a dual-path gate transformer to fuse and enhance attention features, and a refined output header to reconstruct sleep scoring. We perform experiments using three publicly available datasets (SleepEDF-ST, SleepEDF-SC, and SHHS). Experimental results indicate the superiority of MHFNet over baseline approaches in cross-validation. Moreover, at the individual level, MHFNet yielded an average R 2 score improvement of 9% in the testing dataset compared to state-of-the-art models, paving the way for its applications in real-world sleep medicine. © 2013 IEEE.

关键词： Sleep research

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Explainable Hybrid Model for Presaging Tumor Stage Classification with Survival Survey

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SN computer science 2025年第4期6卷 1-24页

作者： Manna, Sweta Mistry, Sujoy Zhao, Zhongming Dahal, Keshav Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Nadia W.B Kolkata 741249 India Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX United States School of Engineering and Computing University of the West of Scotland Paisley PA1 2BE United Kingdom

Tumor stage classification with a survival survey during detection time is the most crucial part of cancer treatment. The survivability period is directly associated with the early detection of the stage. This study developed a hybrid model to accurately classify the tumor (T) stages with the survivability analysis. The study aims to provide an explainable Artificial Intelligence framework (such as SHAP, and SHAPASH), through which the trustability and interpretability of the proposed model can increase. Along with the XAI, this hybrid approach uses two statistical models, ANOVA and LASSO with the standard TNM (tumor, node, metastasis) method for the T-stage classification. Afterward, the survivability analysis for each T stage is shown by the Kaplan–Meier (KM) method, proportion hazard ratio (Cox). Where KM is used to represent the survivability of individual tumor stages in months and the Cox regression shows the risk factors of the events. Initially, the proposed model was examined on a full dataset that contained malignant and benign data for evaluating the performance of the model, therefore stage classification for T1, T2, and T3 performs for malignant data. The K-fold and repeated-stratified K-fold cross-validation are used to measure the accuracy of the whole dataset for lung and breast cancer. For lung cancer, maximum accuracy was attained by MLP–97.64 in K-fold cross-validation. In breast cancer maximum accuracy achieved by GPC-99.57 in repeated-stratified K-fold cross validation. The results of both datasets are compared with the state-of-the-art models. The study contributed by representing the significance of individual features for tumor staging using the advanced approach of XAI, the SHAPASH interface. The accuracy of the T1, T2, and T3 stages based on malignant data was demonstrated with various classifier models with cross-validation. Finally, it is expected that executing that approach will improve patient outcomes. By using, advanced artificial in

关键词： ANOVA Breast cancer Deep learning Explainable artificial intelligence F-score Gaussian process classification LASSO Lung cancer Machine learning Random-Forest Shapely additive explanation XGBoost

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Deep-Source EEG Generation with Deep Learning from Different Configurations of Surface EEG

Deep-Source EEG Generation with Deep Learning from Different...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jiadao Zou Qingxue Zhang Luddy School of Informatics Computing and Engineering Indiana University Department of Computer Science and Department of Biomedical Engineering Indiana University School of Informatics Computing and Engineering Indiana University Luddy Indianapolis

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is difficult. In this study, we target the brain electroencephalogram (EEG) application, and investigate the deep-source EEG generation from surface EEG towards convenient big data. The deep learning algorithm has been developed to mine different configurations of the surface EEG streams, including the single-channel and multi-channel cases, for deep-source EEG generation. Promising experiments on the epilepsy application have been conducted, demonstrating the great promise of deep-learning-empowered deep-source EEG generation. This study will greatly advance brain dynamics mining towards smart consumer technology.

关键词： Deep learning Heuristic algorithms Smart healthcare Epilepsy Big Data Electroencephalography Streams Consumer electronics

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Hierarchical Multimodal Decoupling-Fusion Framework for offline Multiple Appropriate Facial Reaction Generation

Hierarchical Multimodal Decoupling-Fusion Framework for offl...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Lv, Qincheng Liu, Xiaofeng Li, Jie Ni, Rongrong Xue, Pujun Song, Siyang College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350368741

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals' facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions. © 2025 IEEE.

关键词： 2D 3D facial behaviour fusion Facial identity decoupling Multiple Appropriate Facial Reaction Generation

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Personalizing Federated Instrument Segmentation With Visual Trait Priors in Robotic Surgery

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IEEE Transactions on biomedical engineering 2025年 PP卷 PP页

作者： Xu, Jialang Wang, Jiacheng Yu, Lequan Stoyanov, Danail Jin, Yueming Mazomenos, Evangelos B. University College London UCL Hawkes Institute The Department of Medical Physics and Biomedical Engineering London United Kingdom Xiamen University Department of Computer Science School of Informatics Xiamen China The University of Hong Kong Department of Statistics and Actuarial Science Hong Kong National University of Singapore Department of Biomedical Engineering The Department of Electrical and Computer Engineering Singapore

Personalized federated learning (PFL) for surgical instrument segmentation (SIS) is a promising approach. It enables multiple clinical sites to collaboratively train a series of models in privacy, with each model tailored to the individual distribution of each site. Existing PFL methods rarely consider the personalization of multi-headed self-attention, and do not account for appearance diversity and instrument shape similarity, both inherent in surgical scenes. We thus propose PFedSIS, a novel PFL method with visual trait priors for SIS, incorporating global-personalized disentanglement (GPD), appearance-regulation personalized enhancement (APE), and shape-similarity global enhancement (SGE), to boost SIS performance in each site. GPD represents the first attempt at head- wise assignment for multi-headed self-attention personalization. To preserve the unique appearance representation of each site and gradually leverage the inter-site difference, APE introduces appearance regulation and provides customized layer- wise aggregation solutions via hypernetworks for each site's personalized parameters. The mutual shape information of instruments is maintained and shared via SGE, which enhances the cross-style shape consistency on the image level and computes the shape-similarity contribution of each site on the prediction level for updating the global parameters. PFedSIS outperforms state-of-the-art methods with +1.51% Dice, +2.11% IoU, -2.79 ASSD, -15.55 HD95 performance gains. © 1964-2012 IEEE.

关键词： Robotic surgery

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The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment

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Journal of molecular neuroscience : MN 2025年第2期75卷 66页

作者： Mahinaz A Mashhour Ibrahim Youssef Manal Abdel Wahed Mai S Mabrouk Biomedical Engineering Department Misr University for Science and Technology Giza Egypt. mahinaz.mashhour@must.edu.eg. Systems and Biomedical Engineering Department Cairo University Giza Egypt. Center for Informatics Science (CIS) School of Information Technology and Computer Science Nile University Giza Egypt.

Imaging genetics is one of the important keys to precision medicine that leads to personalized treatment based on a patient's genetics, phenotype, or psychosocial characteristics. It deepens the understanding of the mechanisms through which genetic variations contribute to neurological and psychiatric disorders. This systematic review overviews the methods and applications of imaging genetics in the context of neurological diseases, mentioning its potential role in personalized medicine. Following PRISMA guidelines, this review systematically analyzes 28 studies integrating genetic and neuroimaging data to explore disease mechanisms and their implications for precision medicine. Selected research included multiple neurological disorders, including frontotemporal dementia, Alzheimer's disease, bipolar disorder, schizophrenia, Parkinson's disease, and others. Voxel-based morphometry was the most common imaging technique, while frequently examined genetic variants included APOE, C9orf72, MAPT, GRN, COMT, and BDNF. Associations between these variants and regional gray matter loss (e.g., frontal, temporal, or subcortical regions) suggest that genetic risk factors play a key role in disease pathophysiology. Integrating genetic and neuroimaging analyses enhances our understanding of disease mechanisms and supports advancements in precision medicine.

关键词： Imaging genetics Neurological diseases Precision medicine Radiogenomics Voxel-based morphometry

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Holistic Pose Estimation and Dynamic Motion Analysis for Telerehabilitation of Physically Disabled Individuals

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IEEE Access 2025年 13卷 81279-81297页

作者： Kamal, Aleena Najam, Shaheryar Alshehri, Mohammed AlQahtani, Yahya Alshahrani, Abdulmonem Alabdullah, Bayan Park, Jeongmin Riphah International University Department of Biomedical Engineering Islamabad Pakistan Bahria University Department of Electrical Engineering Islamabad Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia King Khalid University Department of Informatics and Computer Systems Abha61421 Saudi Arabia College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Department of Information Systems P.O. Box 84428 Riyadh11671 Saudi Arabia Tech University of Korea Department of Computer Engineering 237 Sangidaehak-ro Gyeonggi-do Siheung-si15073 Korea Republic of

Telerehabilitation systems leveraging depth video analysis provide an effective solution for remote physiotherapy, particularly for individuals with physical disabilities. This study presents an advanced exercise classification framework that integrates multi-modal feature extraction and attention-based transformation to enhance rehabilitation monitoring. The proposed pipeline begins with depth image preprocessing, followed by human detection using a pre-trained Histogram of Oriented Gradients (HOG)-Support Vector Machine (SVM) model. The human silhouette is segmented using the GrabCut algorithm, enabling robust region-of-interest extraction. We propose a novel Lightweight Two-tier Key Body Point Detection (LT-KBPD) algorithm to efficiently and accurately identify key skeletal points, which are then used to extract both static and dynamic kinematic features. In parallel, silhouette-based analysis is performed, where shape descriptors, dense optical flow, Gaussian Mixture Model (GMM)-based body part segmentation, and contour analysis extract spatial and motion-related features. The extracted feature sets are fused into a comprehensive feature vector and further refined using an attention-based transformation mechanism to highlight salient features relevant to exercise classification. Finally, a Long Short-Term Memory (LSTM) network is employed to model temporal dependencies and classify exercises with high accuracy. The proposed approach is validated on three benchmark depth-video datasets: Kimore, K3DA and MEx - Multi-modal Exercise Dataset, achieving classification accuracies of 92.19%, 91.35%, and 85.51%, respectively. These results demonstrate the system’s effectiveness in accurately recognizing rehabilitation exercises for individuals with physical disabilities. Future work aims to enhance the adaptability of the system through personalized rehabilitation feedback and improved temporal modeling techniques. © 2013 IEEE.

关键词： Motion estimation

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FEDERATED INVERSE PROBABILITY TREATMENT WEIGHTING FOR INDIVIDUAL TREATMENT EFFECT ESTIMATION

arXiv

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arXiv 2025年

作者： Yin, Changchang Chen, Hong-You Chao, Wei-Lun Zhang, Ping Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Computer Science and Engineering Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital’s data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the intensive care unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets and the results show that FED-IPTW outperform state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage. © 2025, CC BY-NC-SA.

关键词： Inverse problems

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