IEEE Open Journal of Engineering in Medicine and Biology最新文献

An Improved Beta Burst Extraction for Chip-Based Deep Brain Stimulation With Real-Time Model Updating. 一种改进的基于芯片的脑深部刺激β爆发提取方法及实时模型更新。

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-05-25 eCollection Date: 2026-01-01 DOI: 10.1109/OJEMB.2026.3695567

Yi-Huan Ou-Yang, Hsiao-Chun Lin, Chi-Wei Huang, Chung-Yu Wu, Ming-Dou Ker, Chen-Yi Lee

{"title":"An Improved Beta Burst Extraction for Chip-Based Deep Brain Stimulation With Real-Time Model Updating.","authors":"Yi-Huan Ou-Yang, Hsiao-Chun Lin, Chi-Wei Huang, Chung-Yu Wu, Ming-Dou Ker, Chen-Yi Lee","doi":"10.1109/OJEMB.2026.3695567","DOIUrl":"10.1109/OJEMB.2026.3695567","url":null,"abstract":"Goal: Existing beta burst detection algorithms for closed-loop deep brain stimulation (DBS) are computationally complex, limiting their use in implantable devices. We aimed to develop an improved beta burst extraction algorithm for chip-based DBS devices with real-time model updating. Methods: Building on an established beta burst detection method, we proposed a sliced mechanism for peak frequency finding and modified burst extraction for information sharing with real-time model updating. Results: Testing on rat electrocorticographic (ECoG) recordings showed that the proposed algorithm maintains a strong correlation ( 0.89 0.06) with the conventional method, with a 53.3 reduction in computational complexity for peak frequency finding. Conclusions: Integrating this improved beta burst detection into chip-based DBS devices represents a key algorithmic advancement toward adaptive neuromodulation therapies. The strong correlation and reduced complexity validate our proposal for real-time neural biomarker tracking, facilitating hardware and chip implementation, and advancing the development of implantable systems.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"214-222"},"PeriodicalIF":3.1,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13278738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148296606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disk Harmonic Mapping of Cranial Surfaces for Fracture Visualization. 用于骨折可视化的颅面盘谐映射。

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-04-06 eCollection Date: 2026-01-01 DOI: 10.1109/OJEMB.2026.3681346

Nicolas Hadjittoouli, Costas Pitris

{"title":"Disk Harmonic Mapping of Cranial Surfaces for Fracture Visualization.","authors":"Nicolas Hadjittoouli, Costas Pitris","doi":"10.1109/OJEMB.2026.3681346","DOIUrl":"10.1109/OJEMB.2026.3681346","url":null,"abstract":"Goal: Skull fractures, especially those involving the cranial base and facial regions, present significant diagnostic challenges due to the skull's complex anatomy and subtle radiographic findings. Accurate detection requires repeated and meticulous examination of multiple CT slices, which is a significant cognitive burden, and requires considerable interpretation time. The primary objective of this study is to develop a visualization flattening technique that effectively transforms the curved skull surface into planar representations that enhance fracture features. Methods: A novel visualization process was developed that extracted the cranial surface and subsurface layers from head CT scans and used disk harmonic mapping to generate flattened representations of the lower, upper, occipital, and frontal hemispheres of the skull. The technique was applied to nine cases from the CQ500 dataset, with varying levels of inter-reader agreement, or lack thereof, among the original radiologists who interpreted the dataset. These cases encompass both straightforward and diagnostically challenging fractures that exemplify the advantages of the proposed methodology. Results: The flattened views unwrapped the fractures into continuous, high contrast features, with improved conspicuity compared to the fragmented appearance across multiplanar reconstruction slices. Comparison with existing skull visualization methods, the proposed technique demonstrated high contrast of fractures features, and delineation between emissary veins, with less distortion and high preservation of anatomical continuity. Conclusions: Disk harmonic flattening offers a new approach to skull fracture visualization, providing radiologists and emergency department staff with a valuable addition to the conventional radiological tools, particularly in diagnostically challenging cases.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"139-145"},"PeriodicalIF":3.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13175593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147964541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When Kids Radiate: Low-Resolution Thermography for Total Energy Expenditure Estimation in Pediatric Patients - A Proof of Concept 当孩子辐射：低分辨率热成像总能量消耗估算儿科患者-一个概念的证明

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-02-23 DOI: 10.1109/OJEMB.2026.3667036

Sofiane Beloucif;Mario Francisco Munoz;Kevin Albert;Louise Faineant;Rita Noumeir;Philippe Jouvet

{"title":"When Kids Radiate: Low-Resolution Thermography for Total Energy Expenditure Estimation in Pediatric Patients - A Proof of Concept","authors":"Sofiane Beloucif;Mario Francisco Munoz;Kevin Albert;Louise Faineant;Rita Noumeir;Philippe Jouvet","doi":"10.1109/OJEMB.2026.3667036","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667036","url":null,"abstract":"<italic>Goal: Remote metabolic monitoring is a growing field in pediatric care, aiming to reduce invasive procedures while ensuring continuous assessment. However, clinical adoption remains limited by occlusions, poor image quality, and the scarcity of annotated data. In this study, we propose a framework based on deep learning to estimate total energy expenditure (TEE) in pediatric patients using low-resolution thermography. Our pipeline uses a UNet segmentation model trained to isolate anatomically relevant regions despite visual noise and occlusions. Radiative heat transfer computations are then applied to derive energy expenditure metrics. We tested our method in a cohort of 116 pediatric patients, achieving a mean TEE of 1547 kcal/m<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>/day and a mean absolute error of 279 kcal/m<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>/day. These results highlight the feasibility of thermography as a noninvasive, scalable alternative for metabolic monitoring in Pediatric Intensive Care Units (PICUs), especially in data-constrained environments.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"47-53"},"PeriodicalIF":2.9,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11407455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contactless Sleep Staging With Radar: A Transfer Learning Approach 雷达非接触式睡眠分期：一种迁移学习方法

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-02-23 DOI: 10.1109/OJEMB.2026.3667047

Daniel Krauss;Robert Richer;Nils Albrecht;Jelena Jukic;Carlos Herrera Krebber;Paul Zwiessele;Alexander German;Alexander Koelpin;Martin Regensburger;Jürgen Winkler;Bjoern M. Eskofier

{"title":"Contactless Sleep Staging With Radar: A Transfer Learning Approach","authors":"Daniel Krauss;Robert Richer;Nils Albrecht;Jelena Jukic;Carlos Herrera Krebber;Paul Zwiessele;Alexander German;Alexander Koelpin;Martin Regensburger;Jürgen Winkler;Bjoern M. Eskofier","doi":"10.1109/OJEMB.2026.3667047","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667047","url":null,"abstract":"Accurate sleep monitoring is essential to assess sleep quality and diagnose sleep disorders. Although sleep laboratories provide precise assessments, they are expensive, labor-intensive, and unsuitable for long-term or large-scale monitoring. Radar-based sensing offers a fully contactless alternative, enabling unobtrusive real-world sleep monitoring. However, the lack of large, labeled datasets has limited the development of robust sleep stage classification models. We address this with transfer learning to improve classification accuracy and generalization to unseen participants within the radar cohort. An LSTM model was pretrained on movement, HRV, and respiratory features from the MESA Sleep dataset (<inline-formula><tex-math>$>$</tex-math></inline-formula>1,100 participants) and fine-tuned using radar data from 44 synchronized polysomnography recordings. Transfer learning increased the Matthews Correlation Coefficient from 0.25 to 0.47 (five-class staging), particularly for Wake, N3, and REM sleep. Future work should explore domain-adaptation across modalities and cohorts. Our results highlight the potential of radar-based sleep analysis for scalable, contactless long-term sleep monitoring.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"54-62"},"PeriodicalIF":2.9,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distinguishing Gait Patterns in PD Patients Under Different Treatments via Recurrence Plots and Vision Transformer Fusion 基于复发图和视觉变换融合的PD患者不同治疗方式的步态特征分析

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-02-23 DOI: 10.1109/OJEMB.2026.3667045

Vasileios Skaramagkas;Georgios Karamanis;Iro Boura;Chariklia Chatzaki;Cleanthe Spanaki;Zinovia Kefalopoulou;Manolis Tsiknakis

{"title":"Distinguishing Gait Patterns in PD Patients Under Different Treatments via Recurrence Plots and Vision Transformer Fusion","authors":"Vasileios Skaramagkas;Georgios Karamanis;Iro Boura;Chariklia Chatzaki;Cleanthe Spanaki;Zinovia Kefalopoulou;Manolis Tsiknakis","doi":"10.1109/OJEMB.2026.3667045","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3667045","url":null,"abstract":"<italic>Goal: This study aims to develop an innovative gait analysis framework using recurrence plots (RPs) to differentiate gait patterns between Parkinson's disease (PD) patients under varying treatment regimes and healthy individuals. <italic>Methods: Pressure sensor data were transformed into RPs and analyzed using a Vision Transformer (ViT) model with multiple fusion strategies. To address class imbalance, a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) was employed to generate synthetic gait data. Four ViT-based fusion architectures were investigated and evaluated across multi-class and binary classification tasks. <italic>Results: The dual ViT stream with late fusion achieved the highest accuracy in multi-class classification (94.58%), while the cross-attention fusion model outperformed others in binary classification tasks. <italic>Conclusions: The findings indicate that gait characteristics captured via RPs can effectively distinguish between PD patients under different treatments and healthy controls. This approach provides a data-driven pathway for objective and individualized assessment of PD therapies, potentially supporting improved clinical decision-making.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"63-69"},"PeriodicalIF":2.9,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2025 Index IEEE Open Journal of Engineering in Medicine and Biology Vol. 6 IEEE开放医学与生物工程杂志第6卷

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-21 DOI: 10.1109/OJEMB.2026.3656806

引用次数: 0

3D Engineered Biomimetic Platform for Characterization of Collective Invasion, Tumor Emboli Formation, and Lymphatic Dissemination 三维工程仿生平台表征集体侵袭，肿瘤栓塞形成和淋巴传播

IF 2.9

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-02-23 DOI: 10.1109/OJEMB.2026.3666977

Caroline Way;Ralph F. Erdmann;Larisa Gearhart-Serna;Pritha Pai;Natalia Roman;Bruce Klitzman;Gayathri R. Devi

{"title":"3D Engineered Biomimetic Platform for Characterization of Collective Invasion, Tumor Emboli Formation, and Lymphatic Dissemination","authors":"Caroline Way;Ralph F. Erdmann;Larisa Gearhart-Serna;Pritha Pai;Natalia Roman;Bruce Klitzman;Gayathri R. Devi","doi":"10.1109/OJEMB.2026.3666977","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3666977","url":null,"abstract":"<italic>Goal: Emerging evidence in diverse tumor types establishes a link between lymphatic dissemination and collective tumor cell invasion. To simulate the biomechanical features of the tumor-lymphatic microenvironment, we developed a 3D tumor-lymphatic architecture biomimetic (T-LAB) platform. <italic>Methods: Mathematical and computational fluid dynamics modeling were used to determine the fluid flow, oscillatory flow-induced shear stress, and system pressure in the 3D-printed macrofluidics platform. <italic>Results: Various human breast cancer cell lines and human dermal lymphatic endothelial cells (HDLEC) were seeded on a matrix in the T-LAB and imaged for up to 96 h to assess cell morphology, viability, migration, and invasion. Co-culture of inflammatory breast cancer cells with HDLEC in the T-LAB, determined to simulate the fluidic properties of the tumor lymphatic microenvironment, demonstrated tumor cell clusters/emboli formation and collective invasion similar to the clinicopathological features observed in patients. <italic>Conclusions: The 3D T-LAB model developed here can be used to culture any type of tumor cell to study topographical features that impact tumor-lymphatic interface, collective invasion, and lymphatic dissemination.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"119-127"},"PeriodicalIF":2.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation 染色一致性学习：处理染色变化的自动数字病理分割。

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-04-23 DOI: 10.1109/OJEMB.2026.3687108

Michael Yeung;Todd Watts;Sean YW Tan;Peiyuan Jing;Pedro F. Ferreira;Andrew D. Scott;Sonia Nielles-Vallespin;Guang Yang

引用次数: 0

Continuous Monitoring in Implantable Cardiac Devices: Electro-Quasistatic Human Body Communication – Challenges and Opportunities 植入式心脏装置的连续监测：准静态人体通信-挑战与机遇

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-08-11 DOI: 10.1109/OJEMB.2026.3722470

Ayan Biswas;Anyu Jiang;Arunashish Datta;Asif Iftekhar Omi;Baibhab Chatterjee;Shreyas Sen

{"title":"Continuous Monitoring in Implantable Cardiac Devices: Electro-Quasistatic Human Body Communication – Challenges and Opportunities","authors":"Ayan Biswas;Anyu Jiang;Arunashish Datta;Asif Iftekhar Omi;Baibhab Chatterjee;Shreyas Sen","doi":"10.1109/OJEMB.2026.3722470","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3722470","url":null,"abstract":"Cardiovascular diseases are the leading cause of global mortality, with millions of patients relying on implantable pacemakers. Finite battery energy fundamentally limits remote monitoring. Using open-source manufacturer longevity data, under stated parameter assumptions, we develop an energy-balance model to show a stringent <inline-formula><tex-math>$approx 130$</tex-math></inline-formula> J/year communication budget, too low for continuous monitoring with conventional RF links. We investigate electro-quasistatic human body communication (EQS-HBC) as an alternative physical layer for pacemakers, combining a systematic review of implant-grade transceivers with a proof-of-concept channel feasibility study in a human-torso phantom. Our analyses indicate that, under these modeling assumptions EQS-HBC could, in principle, support 1 kSps, 16-bit equivalent continuous streaming at <inline-formula><tex-math>$approx 2,mu mathrm{W}$</tex-math></inline-formula> (<inline-formula><tex-math>$>!!100times$</tex-math></inline-formula> more energy-efficient than RF) while remaining within safety standards and preserving <inline-formula><tex-math>$>!!14$</tex-math></inline-formula>-year lifetime, outlining design guidance for future implantable cardiac devices.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"324-331"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11651348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spine Sound Classification: An Explainable Machine Learning Approach 脊椎声音分类：一种可解释的机器学习方法

IF 3.1

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2026-01-01 Epub Date: 2026-09-09 DOI: 10.1109/OJEMB.2026.3732585

Abdallah Hussein Sham;H. Gültekin;İ. Uzunoğlu;E. K. Öztürk;E. Çetin;E. Acaroğlu;A. E. Çetin;Gholamreza Anbarjafari

{"title":"Spine Sound Classification: An Explainable Machine Learning Approach","authors":"Abdallah Hussein Sham;H. Gültekin;İ. Uzunoğlu;E. K. Öztürk;E. Çetin;E. Acaroğlu;A. E. Çetin;Gholamreza Anbarjafari","doi":"10.1109/OJEMB.2026.3732585","DOIUrl":"https://doi.org/10.1109/OJEMB.2026.3732585","url":null,"abstract":"Low-cost, non-invasive screening tools for lumbar degenerative disease could complement imaging in resource-limited settings. This study presents a machine-learning framework to classify spine sounds recorded during lumbar motion and distinguish subjects with MRI-confirmed intervertebral disc and facet joint degeneration from asymptomatic controls. From each recording, we extract a 127-dimensional feature vector (MFCC statistics and temporal/spectral energy descriptors) and apply L1-norm-inducing kernel PCA (min and min-sign kernels) to obtain a compact, outlier-robust latent representation. Classification is performed using standard learners (SVM, Random Forest, Gradient Boosting, and KNN) under leave-one-patient-out (LOPO) cross-validation. The best configuration (min-kernel L1-kPCA with 20 components) achieves 93.3% accuracy with 93.0% sensitivity and 0.952 specificity (95% Wilson CI: 0.842–0.987) with an SVM classifier under rigorous LOPO cross-validation, outperforming the no-kPCA baseline (90.8% accuracy) and matching the neural network-based solution (93.3%). These results suggest that robust nonlinear feature compression can substantially improve spine sound classification and motivate external validation toward point-of-care screening.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"7 ","pages":"316-323"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11684366","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0