MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING医学与生物工程及计算
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING(英文缩写 MED BIOL ENG COMPUT),ISSN 0140-0118,eISSN 1741-0444,中文译名:医学与生物工程及计算 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-28 至 2026-09-28，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 3.079 | Q2 |
| 2022 | 3.200 | Q2 |
| 2023 | 2.600 | Q2 |
| 2024 | 2.600 | Q2 |
| 2025 | 3.100 | Q2 |
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 最新收录文献
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1. Development and validation of an integrated wearable system for continuous cardiorespiratory monitoring using photoplethysmography and optical respiratory detection.
PMID:日期:2026-09-25Cardiovascular diseases remain the leading cause of mortality worldwide, with ischaemic heart disease the leading contributor and acute myocardial infarction accounting for the majority of ischaemic heart disease deaths. This study presents the development and technical validation of a low-cost, integrated wearable system for multi-parameter cardiorespiratory monitoring designed to support heart-attack detection algorithms. The system comprises two devices: a wristband incorporating a MAX30105 photoplethysmography sensor for cardiac pulse-wave and SpO[Formula: see text] acquisition, and a chest band featuring novel optical respiratory detection and an MPU6050 inertial measurement unit for respiratory rate and movement monitoring. Both devices use ESP32-C3 microcontrollers with Bluetooth Low Energy connectivity to a custom mobile application enabling real-time visualization and cloud-based storage. Validation was conducted against clinical-grade reference equipment (ADInstruments physiograph and EDAN iM8 monitor) in 43 healthy volunteers, yielding 50 acquisitions: 30 at rest and 20 post-physical activity. This is a technical feasibility study in healthy young volunteers; validation in clinical populations remains future work. Heart rate showed the closest agreement with the reference (percentage error ±1.94%, [Formula: see text] post-activity; limits of agreement [Formula: see text] to 4.21 BPM), followed by respiratory rate (±3.47%, [Formula: see text]). SpO[Formula: see text] error remained within tolerance (1.64% at rest, 2.98% post-activity) but showed weak correlation with the reference owing to range restriction in a healthy cohort, and is not yet suitable for applications requiring absolute saturation values. This study demonstrates that a low-cost, multi-sensor wearable system can achieve accuracy meeting the prespecified feasibility criterion ([Formula: see text]5% error) in a healthy young cohort, establishing technical feasibility as a prerequisite to validation in the cardiovascular patient populations for which the system is ultimately intended.
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2. Changes in temporal and spatial gait coordination during the six‑minute walk test in people with multiple sclerosis.
PMID:日期:2026-09-20Although gait coordination impairments represent a critical aspect of walking dysfunction in multiple sclerosis (MS), their variation during prolonged walking remains relatively unexplored. This study investigated changes in gait coordination during the six-minute walk test (6MWT) by jointly analyzing temporal and spatial measures of inter-limb and inter-joint coordination. Sixty-five people with MS and 43 matched unaffected individuals performed the 6MWT while kinematic data were acquired using an inertial measurement unit-based motion capture system. Coordination was quantified using the Phase Coordination Index and cyclogram-based geometric features, computed on a minute-by-minute basis. Results showed that people with MS exhibited significantly impaired coordination compared with unaffected individuals across most metrics from the beginning of the test. While both groups demonstrated progressive changes over time, people with MS showed limited additional deterioration during prolonged walking, suggesting a ceiling effect related to baseline neuromotor impairment and reduced adaptive capacity. Inter-joint coordination changes were most evident at the knee-ankle joint. Temporal and spatial measures provided complementary insights, revealing joint- and domain-specific behaviors missed by single-metric approaches. These findings support multimodal coordination analysis to improve gait assessment and guide rehabilitation in MS.
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3. Lumina-Net: temporal learning with feature fusion for endoscopic artifact removal.
PMID:日期:2026-09-20Gastrointestinal (GI) endoscopy is widely used for diagnosis and minimally invasive therapy, yet specular reflections frequently saturate tissue appearance and introduce temporal flicker, degrading both clinical inspection and downstream computational analysis. This study proposes Lumina-Net, a mask-guided video inpainting framework whose spatiotemporal Transformer with overlapping tokens aggregates complementary information across consecutive frames. The decoder incorporates two lightweight modules: Variance-Guided Feature Modulation (VGFM), which recalibrates features using channel statistics to handle mixed-scale specular highlights, and a parameter-free Multi-Scale Energy Free-Space Attention (MS-EFSA) mechanism that derives spatial weights from feature energy to preserve mucosal structures. On the HyperKvasir and GastroHUN datasets, Lumina-Net achieves a peak signal-to-noise ratio (PSNR) of 30.20 dB and reduces the mean squared error (MSE) by approximately 5.3% compared with the strongest baseline, while running at 27 frames per second (FPS) on a single GPU. In a blinded evaluation, clinical experts consistently prefer the visual quality of the proposed method, and improved monocular depth estimation on processed sequences further demonstrates downstream utility. These results indicate that Lumina-Net provides temporally stable specular reflection removal for reliable clinical observation and downstream tasks such as robotic-assisted intervention.
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5. Multi-feature fusion with bidirectional cross-modal attention for joint multi-task classification of heart sounds.
5. 基于双向跨模态注意力的多特征融合用于心音联合多任务分类PMID:日期:2026-09-15Automated heart sound auscultation is crucial for early cardiovascular disease screening, yet existing multi-task learning approaches face challenges in cross-modal feature fusion, signal quality coupling, and heterogeneous data optimization. This paper proposes a robust multi-feature fusion framework that simultaneously performs murmur detection, clinical outcome prediction, and signal quality assessment. We extracted raw waveforms, Mel-frequency cepstral coefficients (MFCCs), and statistical features and deeply integrated them via a bidirectional cross-modal attention mechanism. Crucially, to enable joint training on heterogeneous datasets with missing labels, we introduced a task masking mechanism alongside a homoscedastic uncertainty-based dynamic weighting strategy to resolve multi-task loss conflicts. When evaluated on the CirCor DigiScope and a multi-source signal quality dataset, the proposed framework demonstrated highly competitive performance. It achieved a murmur detection weighted accuracy of 0.766±0.026 and reduced the clinical screening cost to 10490±491, which was far below the PhysioNet Challenge 2022 average. Concurrently, the model yielded a cost of 10553±1134 for clinical outcome prediction and secured a macro-F1 score of 0.845±0.015 for signal quality assessment. By explicitly co-optimizing diagnostic tasks and signal quality, our method provides an efficient, low-cost solution tailored for primary healthcare and noisy real-world clinical settings.
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6. A unified medical image segmentation evaluation method combining multi-metrics and confidence.
PMID:日期:2026-09-14The advancement of evaluation methodologies for medical image segmentation models has not kept pace with the development of the models themselves. Current technologies encounter challenges related to complexity and uncertainty, which hinder their efficacy in guiding clinical practice. This study aims to provide a comprehensive framework for quantifying the overall capabilities of medical image segmentation models. We propose a unified approach that facilitates the simultaneous assessment of prediction accuracy and reliability. Based on monotonic rank agreement, we integrate various accuracy metrics alongside confidence levels derived from multi-organ segmentation results to compute a final comprehensive score. The obtained scores can be used for an intuitive comparison of models: models with higher scores mean both high accuracy and high reliability, making them more clinically applicable. We conducted extensive experiments on six different medical image segmentation models and compared them from four perspectives: accuracy, reliability estimation, usable region estimation, and our proposed assessment pipeline. Experimental results verify that our method delivers more interpretable quantitative metrics to assess the practical comprehensive performance of segmentation models than the single-metric methods. The code has been released on GitHub ( https://github.com/SCUT-ML-GUO/MOMAI ).
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7. Study on mental fatigue detection of crane operators based on frequency and channel attention spiking neural network with multivariate variational mode decomposition in real construction environments.
PMID:日期:2026-09-14To address the issues of strong noise interference and low detection accuracy of mental fatigue in tower crane operators under complex construction site environments, this study proposes a novel mental fatigue recognition method based on a frequency and channel attention spiking neural network with multivariate variational mode decomposition (MVMD-FCASNN). The method employs multivariate variational mode decomposition (MVMD) to jointly decompose multichannel electroencephalogram (EEG) signals and extract frequency-aligned intrinsic mode functions (IMFs), thereby enhancing feature separability and robustness against interference. An IMF attention mechanism is designed to adaptively evaluate the contribution of distinct frequency bands, while a channel attention module emphasizes critical brain regions, particularly the frontal and central areas, to strengthen spatial representation. The extracted features are further processed using a spiking neural network (SNN) to preserve the temporal dynamics of EEG signals and improve performance under noisy conditions. Experimental results demonstrate that the proposed MVMD-FCASNN model achieves an accuracy of 98.81% in mental fatigue classification tasks and maintains strong performance even under high noise conditions (-6 dB), significantly outperforming traditional methods. This approach offers a reliable and efficient solution for real-time mental fatigue monitoring in high-noise construction scenarios.
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8. Gastric mucosa intestinal metaplasia segmentation and grading via transformer-CNN fusion architecture: an interpretable digital pathology diagnostic framework.
PMID:日期:2026-09-12Intestinal metaplasia (IM) is a critical stage in the precancerous lesions of gastric cancer, and its accurate grading is essential for clinical risk stratification and early intervention. However, traditional diagnosis relies on the subjective assessment of pathologists, which suffers from inefficiency and low consistency. To address these limitations, this study proposes a deep learning framework based on whole slide images (WSI), utilizing multi-scale image patch cropping and a Transformer-CNN hybrid network (UDTransNet) to achieve pixel-level segmentation of intestinal metaplasia regions in gastric mucosa. The framework quantifies the area proportion of metaplastic glands according to the Sydney system criteria, enabling automated grading of IM severity. The results show that on the internal test set, the model achieved a segmentation performance with a Dice coefficient of 0.9698 and a grading accuracy of 0.8879 (Kappa value 0.85), demonstrating high consistency with pathological experts' diagnoses. The model significantly outperformed the diagnostic consistency between junior and intermediate pathologists (Kappa 0.67-0.82). Additionally, the model achieved specificity in identifying completely normal tissues, with a recall rate of 92.1% and an F1 score of 95.9%, effectively assisting clinical "negative exclusion" workflows. This study is the first to establish a digital mapping between pathological morphological features and the Sydney system criteria, addressing the challenges of strong subjectivity and low reproducibility in traditional diagnosis. It provides an efficient and interpretable solution for intelligent screening of gastric precancerous lesions.
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9. Predicting 30-day readmission after heart failure hospitalization using interpretable machine learning: evidence from a 20-year population-based rural registry.
PMID:日期:2026-09-07Readmissions after heart failure hospitalization are common, costly, and potentially preventable, but discharge risk stratification remains challenging, particularly in rural health systems with constrained resources. We aimed to develop an interpretable artificial intelligence approach to estimate individual 30-day all-cause readmission risk using routinely collected variables. We analyzed nearly 5,000 heart failure admissions from a non-public regional Spanish registry representing a rural healthcare setting (2000-2019; 8.2% readmitted within 30 days) and not previously used for artificial intelligence modeling. Three machine learning models -random forest, extreme gradient boosting, and support vector machine- were trained, validated, and compared with binary logistic regression. SHAP quantified predictor contributions and assessed the direction and consistency of their effects. Random forest showed the best performance (AUC 0.812, 95% CI 0.744-0.867), outperforming binary logistic regression (AUC 0.686, 95% CI 0.617-0.755). The most influential predictors were admission period, renal dysfunction markers, prior heart failure, age, and length of stay. Interpretable machine learning improved 30-day readmission risk stratification using routine data and provided transparent explanations that could support targeted post-discharge interventions. External validation and prospective impact evaluation are required before clinical implementation.
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10. Analysis of EEG microstate transition patterns and multi-instance learning-based recognition model for depression.
PMID:日期:2026-09-03Accurate identification of major depressive disorder (MDD) and characterization of abnormal higher-order brain-state transitions remain important in electroencephalography (EEG)-based depression research. Although EEG microstate analysis provides a promising high-temporal-resolution tool for probing abnormal brain dynamics, most existing studies focus on static microstate parameters and may overlook higher-order transition structure embedded in microstate sequences. To address this issue, we proposed a rule-driven microstate sequence analysis framework for EEG-based depression recognition. Specifically, the RuleGrowth algorithm was used to mine discriminative microstate transition rules, which were incorporated into an attention-based multiple instance learning model for classification. The proposed method outperformed conventional approaches based on static microstate features on both datasets, achieving accuracy, F1-score, and area under the curve (AUC) values of 96.67%, 94.12%, and 97.23% on the multi-modal open dataset for mental disorder analysis (MODMA) dataset, and 92.67%, 92.78%, and 94.50% on the self-collected dataset. In addition, both datasets showed significant group differences in higher-order transition patterns after false discovery rate (FDR) correction ([Formula: see text]), with 7 and 13 significant rules identified, respectively, and the C→D→B and A→D→B rules consistently showing higher confidence in the MDD group than in healthy controls across both datasets. These findings indicate that MDD is associated with disrupted higher-order brain-state switching dynamics and support the utility of rule-based microstate representations for EEG-based depression analysis.