Biomedical Signal Processing and Control最新文献_第4页

Mathematical modelling of atherogenesis: temperamental endothelial permeability 动脉粥样硬化的数学模型：内皮渗透性的变化

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-10 DOI: 10.1016/j.bspc.2026.109718

Shankar Narayan S , Aishwarya R , Nidhi S Vaishnaw

A significant contributor to the development of atherosclerosis is endothelial dysfunction, which is typified by elevated permeability. In order to understand the intricate interactions among low-density lipoprotein (LDL), cytokines (A), inflammatory immune cells (M), endothelial permeability (E), and vascular remodeling (R), we construct an evolving mathematical model in the present research. Using PID control theory, we introduce a novel approach to modulate endothelial permeability, demonstrating how proportional

(k_{p} = 0.01)

, integral

(k_{i} = 0.001)

, and derivatives

(k_{d} = 0.01)

control terms can stabilize the system and restore endothelial function. Our simulations reveal nonlinear relationships and critical thresholds, where a

10 %

increase in LDL leads to a

25 %

rise in endothelial permeability, highlighting the sensitivity of the endothelium to small changes in LDL levels. Heatmap and other plot analyses further elucidate the system’s dynamics, showing that low levels of LDL (below

2 \times 10^{- 3} g . c m^{- 3}

) and cytokines (below

10^{- 7} g . c m^{- 3}

) are sufficient to induce significant endothelial dysfunction. At higher concentrations, permeability stabilizes near

E \approx 12 \times 10^{- 3} c m^{3} / (g . d a y)

. These findings underscore the importance of early intervention and multi-targeted therapies to mitigate endothelial damage and slow atherosclerosis progression. This study advances our understanding of the molecular mechanisms driving endothelial permeability and provides a computational framework for designing personalised therapeutic strategies.

动脉粥样硬化的一个重要因素是内皮功能障碍，其典型特征是通透性升高。为了了解低密度脂蛋白（LDL）、细胞因子(A)、炎症免疫细胞(M)、内皮通透性(E)和血管重塑(R)之间复杂的相互作用，我们在本研究中构建了一个不断发展的数学模型。利用PID控制理论，我们引入了一种调节内皮通透性的新方法，展示了比例（kp=0.01）、积分（ki=0.001）和导数（kd=0.01）控制项如何稳定系统并恢复内皮功能。我们的模拟揭示了非线性关系和临界阈值，其中LDL增加10%导致内皮通透性增加25%，突出了内皮对LDL水平微小变化的敏感性。热图和其他图分析进一步阐明了系统动力学，显示低水平的LDL（低于2×10-3g.cm-3）和细胞因子（低于10-7g.cm-3）足以诱导显着的内皮功能障碍。在较高浓度下，渗透率稳定在E≈12×10-3cm3/(g.day)附近。这些发现强调了早期干预和多靶向治疗对于减轻内皮损伤和减缓动脉粥样硬化进展的重要性。这项研究促进了我们对驱动内皮细胞渗透性的分子机制的理解，并为设计个性化治疗策略提供了一个计算框架。

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引用次数: 0

TFR-GANNet: Multi-channel time–frequency ridge fusion and CWGAN-GP for sleep arousal identification TFR-GANNet：多通道时频脊融合和CWGAN-GP用于睡眠觉醒识别

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-10 DOI: 10.1016/j.bspc.2026.109846

Wenjuan Lu , Zhongxing Liu , Yu Chen , Jinxi Wang

The precise identification of sleep arousal events is critical for sleep quality assessment and diagnosis of sleep disorders. Current methods face three major limitations: (1) the lack of an effective multi-channel signal fusion method, (2) inadequate handling of class-imbalanced datasets, and (3) reliance on expert annotations, which hinder the clinical applicability of existing methods. To address these limitations, an integrated method named TFR-GANNet was proposed, which incorporates three modules for multi-channel data fusion, data augmentation, and sleep arousal identification. First, the data fusion module implements a novel time–frequency ridge-based multi-channel signal fusion (TFR-MSF) strategy to construct a global time–frequency ridge index map (GTFRIM) by extracting and integrating channel-specific ridge features, enabling physiologically meaningful signal representation. Subsequently, the data augmentation module utilizes a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) to synthesize GTFRIM samples, which effectively balances the data distribution across different classes. Finally, the sleep arousal identification module consists of a dense convolutional network integrated with a multi-head attention mechanism (DMNet), enabling accurate identification of sleep arousal events from GTFRIMs. On the PhysioNet 2018 sleep dataset, TFR-GANNet achieved state-of-the-art performance compared with existing methods, with an accuracy of 0.9556, an AUROC of 0.9699, an F1-score of 0.9575, and an AUPRC of 0.8751. Extensive ablation studies confirmed the individual contributions of each module. The proposed framework advances sleep analysis through an integrated solution to key challenges in data fusion, class imbalance, and annotation scarcity, paving the way for robust applications in clinical and research domains.

准确识别睡眠唤醒事件对睡眠质量评估和睡眠障碍的诊断至关重要。目前的方法面临三个主要的局限性：(1)缺乏有效的多通道信号融合方法；(2)对类别不平衡数据集的处理不足；(3)依赖专家注释，阻碍了现有方法的临床适用性。为了解决这些问题，提出了一种TFR-GANNet集成方法，该方法包括多通道数据融合、数据增强和睡眠唤醒识别三个模块。首先，数据融合模块实现了一种新颖的基于时频脊的多通道信号融合（TFR-MSF）策略，通过提取和整合通道特定的脊特征来构建全局时频脊指数图（GTFRIM），从而实现有生理意义的信号表示。随后，数据增强模块利用带梯度惩罚的条件Wasserstein生成对抗网络（CWGAN-GP）合成GTFRIM样本，有效平衡了不同类别之间的数据分布。最后，睡眠唤醒识别模块由一个与多头注意机制（DMNet）集成的密集卷积网络组成，能够准确识别来自gtfram的睡眠唤醒事件。在PhysioNet 2018睡眠数据集上，与现有方法相比，TFR-GANNet取得了最先进的性能，准确率为0.9556，AUROC为0.9699，f1得分为0.9575，AUPRC为0.8751。广泛的消融研究证实了每个模块的个人贡献。提出的框架通过集成解决数据融合、类不平衡和注释稀缺性等关键挑战来推进睡眠分析，为临床和研究领域的强大应用铺平了道路。

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引用次数: 0

Multimodal deep learning with attention mechanisms for automated detection of lower extremity deep vein thrombosis: Integrating ultrasound, CT, and MRI 多模态深度学习与下肢深静脉血栓自动检测的注意机制：整合超声、CT和MRI

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-10 DOI: 10.1016/j.bspc.2026.109749

Haojie Wang, Yuanhu Jing, Hongyuan Li, Yan Zhang, Congcong Chang, Gongning Shi

This study developed a two-stage cascaded segmentation framework with integrated attention mechanisms (SE/CBAM) that accommodates multimodal inputs from CT, MRI, and ultrasound and demonstrates robustness to missing modalities. The proposed approach was systematically evaluated on data from 322 with deep vein thrombosis (DVT) and compared against representative methods, including nnU-Net, Swin-UNet, UNETR, and MedSAM/EMedSAM. Under standardized preprocessing and five-fold cross-validation, our model achieved a Dice coefficient of 0.873 ± 0.018, IoU of 0.783 ± 0.021, PR-AUC of 0.921, and ROC-AUC of 0.942 on an independent test set, significantly outperforming nnU-Net, Swin-UNet, UNETR, and MedSAM/EMedSAM baselines. Grad-CAM heatmaps showed strong spatial concordance with expert annotations at key anatomical regions. Sensitivity analyses confirmed robustness to hyperparameter variation, noise, and single-modality dropout (<1% fluctuation). To enhance biological interpretability, we integrated transcriptomic (GEO, n = 40), proteomic (PRIDE, n = 30), and metabolomic (MetaboLights, n = 20) datasets, identifying key molecules such as RPS3A, RPL31, and TP53, as well as differential metabolites including oxidized glutathione, succinate, and betaine. These molecular alterations were predominantly enriched in glutathione metabolism, the tricarboxylic acid (TCA) cycle, and inflammation-related pathways (e.g., the IL-17 and TNF signaling axes). Notably, these pathways are directionally consistent with the inflammatory activation and oxidative stress features reflected in the high-risk thrombotic anatomical regions emphasized by the imaging model, thereby providing supportive mechanistic interpretation for the imaging-based recognition results. Collectively, this study establishes a methodological foundation for interpretable AI-driven diagnosis and precision intervention in DVT and demonstrates potential for extension to other vascular diseases.

本研究开发了一个具有综合注意机制（SE/CBAM）的两阶段级联分割框架，该框架可容纳来自CT、MRI和超声的多模态输入，并证明了对缺失模态的鲁棒性。我们对322例深静脉血栓患者的数据进行了系统评估，并与具有代表性的方法（包括nnU-Net、swwin - unet、UNETR和MedSAM/EMedSAM）进行了比较。经过标准化预处理和五重交叉验证，我们的模型在独立测试集上的Dice系数为0.873±0.018，IoU为0.783±0.021，PR-AUC为0.921，ROC-AUC为0.942，显著优于nnU-Net、swun - unet、UNETR和MedSAM/EMedSAM基线。在关键解剖区域，Grad-CAM热图与专家标注的空间一致性较强。敏感性分析证实了对超参数变化、噪声和单模态丢失（<；1%波动）的稳健性。为了提高生物学可解释性，我们整合了转录组学（GEO, n = 40）、蛋白质组学（PRIDE, n = 30）和代谢组学（metabolomics, n = 20）数据集，确定了关键分子，如RPS3A、RPL31和TP53，以及差异代谢物，包括氧化谷胱甘肽、琥珀酸盐和甜菜碱。这些分子改变主要富集于谷胱甘肽代谢、三羧酸（TCA）循环和炎症相关途径（例如，IL-17和TNF信号轴）。值得注意的是，这些途径与成像模型强调的高危血栓解剖区域所反映的炎症激活和氧化应激特征在方向上是一致的，从而为基于成像的识别结果提供了支持性的机制解释。总的来说，本研究为可解释的人工智能驱动的DVT诊断和精确干预奠定了方法学基础，并展示了扩展到其他血管疾病的潜力。

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引用次数: 0

Triplet-branch diffusion model with conditional guidance and boundary enhancement for cervical nucleus segmentation 有条件引导和边界增强的三分支扩散模型用于颈核分割

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-10 DOI: 10.1016/j.bspc.2026.109753

Tianyu Shi , Jia Tang , Yantao Sun , Zhimin Liu

As the fourth most common cancer among women worldwide, cervical cancer diagnosis relies heavily on accurate and automated segmentation of cell nuclei in pathological images for early screening. To address challenges such as blurred boundaries, overlapping cells, and complex background interference in this task, we propose a novel triplet-branch diffusion model which is constructed in three key stages: First, a diffusion backbone network is developed to progressively reconstruct the target structures from noisy masks via a denoising process, integrating a frequency-domain attention mechanism to suppress high-frequency noise. Second, a semantic condition branch based on the U-Net architecture is designed to extract multi-scale image features, which injects anatomical priors into the diffusion backbone through cross-layer connections. Third, an edge guided branch is introduced, which employs a Boundary Attention Module to fuse explicit edge features extracted by the Canny operator into the diffusion backbone, enabling multi-level boundary guidance during the decoding phase. We validate the proposed model on two public datasets and one internal private dataset, achieving Dice coefficients of 94.36%, 95.04%, and 93.16%, respectively—representing improvements of 1.2% to 2.1% over state-of-the-art models in the field. Ablation studies on the proposed modules and loss functions, as well as visual analyses of the reverse diffusion process, further demonstrate the effectiveness of our approach. This method effectively reduces boundary errors in the segmentation of cervical cell nuclei while maintaining high interpretability. It provides potential intelligent diagnostic support for large-scale early screening of cervical cancer. However, further validation of its reliability on multi-center or clinical datasets is necessary.

作为全球第四大女性常见癌症，宫颈癌的诊断在很大程度上依赖于病理图像中细胞核的准确和自动分割，以进行早期筛查。为了解决该任务中存在的边界模糊、细胞重叠和背景干扰复杂等问题，我们提出了一种新的三分支扩散模型，该模型分为三个关键阶段：首先，建立扩散骨干网络，通过去噪过程从噪声掩模中逐步重建目标结构，并集成频域注意机制来抑制高频噪声；其次，设计基于U-Net架构的语义条件分支提取多尺度图像特征，通过跨层连接将解剖先验注入扩散主干；第三，引入边缘引导分支，利用边界注意模块将Canny算子提取的显式边缘特征融合到扩散主干中，实现解码阶段的多级边界引导。我们在两个公共数据集和一个内部私有数据集上验证了所提出的模型，分别获得了94.36%、95.04%和93.16%的Dice系数，比该领域最先进的模型提高了1.2%到2.1%。对所提出的模块和损失函数的烧蚀研究，以及反向扩散过程的可视化分析，进一步证明了我们方法的有效性。该方法在保持较高的可解释性的同时，有效地减少了宫颈细胞核分割的边界误差。它为大规模宫颈癌早期筛查提供了潜在的智能诊断支持。然而，在多中心或临床数据集上进一步验证其可靠性是必要的。

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引用次数: 0

DeepOsteoCls: Deep learning-based framework for Knee Osteoarthritis Classification with qualitative explanations from radiographs and MRI volumes DeepOsteoCls：基于深度学习的膝关节骨关节炎分类框架，从x线片和MRI体积中进行定性解释

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-10 DOI: 10.1016/j.bspc.2026.109819

Akshay Daydar , Arijit Sur , Subramani Kanagaraj , Hanif Laskar

Knee Osteoarthritis (KOA) is a degenerative joint disorder affecting middle-aged and elderly individuals, with its diagnosis facing challenges in achieving objective, transparent quantification and incorporating clinical manifestations, despite advances in deep-learning for medical imaging. To address these issues, in this paper, a deep learning-based hybrid (Convolutional Neural Network (CNN)-Transformer encoder) classification framework, DeepOsteoCls, is proposed to perform binary and multi-class classification of KOA from X-rays and MRI scans from OsteoXRNet and OsteoMRNet models separately, with Gradient-weighted Class Activation Mappings (Grad-CAMs). The Osteoarthritis Edge Detection (OAED) and Multi-Resolution Feature Integration (MRFI) modules are also introduced in the proposed framework to facilitate the extraction of edge-based features from X-ray images and multi-scale regional features from the MRI volume, respectively. Furthermore, a disorder-aware weakly supervised training scheme—Domain Knowledge Transfer and Entropy Regularization (DoKTER) is proposed to enhance the explainability of Radiological KOA (RKOA) diagnosis by predicting the region score and GradCAMs of MRI scans. Comprehensive experiments on the Osteoarthritis Initiative (OAI) dataset demonstrated that the proposed framework achieved a classification accuracy of 72.10% for X-ray and 53.16% for MRI in a multi-class classification task, and 85.74% for X-ray and 81.04% for MRI in a binary classification task, outperforming state-of-the-art models. The DoKTER scheme is found to accurately classify the affected region with 65.15% and 62.5% for the OAI and Multi-Hospital Knee Osteoarthritis (MHKOA) datasets, respectively. Additionally, Femoral Cartilage Thickness (FCT) in non-RKOA subjects can be effectively monitored using the region score, with distinct cut-offs values. The code is available at: https://github.com/adaydar/Deep-OsteoCls

膝关节骨性关节炎（KOA）是一种影响中老年人的退行性关节疾病，尽管在医学成像的深度学习方面取得了进展，但其诊断在实现客观、透明的量化和纳入临床表现方面面临挑战。为了解决这些问题，本文提出了一种基于深度学习的混合（卷积神经网络(CNN)-变压器编码器）分类框架DeepOsteoCls，该框架使用梯度加权类激活映射（梯度- cams）分别对来自骨oxrnet和骨omrnet模型的x射线和MRI扫描的KOA进行二元和多类分类。在该框架中还引入了骨关节炎边缘检测（OAED）和多分辨率特征集成（MRFI）模块，分别用于从x射线图像中提取基于边缘的特征和从MRI体积中提取多尺度区域特征。在此基础上，提出了一种无序感知的弱监督训练方案——领域知识转移和熵正则化（DoKTER），通过预测MRI扫描的区域评分和梯度梯度，提高放射KOA （RKOA）诊断的可解释性。在Osteoarthritis Initiative （OAI）数据集上进行的综合实验表明，该框架在多类别分类任务中，x射线和MRI的分类准确率分别为72.10%和53.16%，在二元分类任务中，x射线和MRI的分类准确率分别为85.74%和81.04%，优于目前最先进的模型。发现DoKTER方案对OAI和多医院膝关节骨关节炎（MHKOA）数据集的影响区域分类准确率分别为65.15%和62.5%。此外，非rkoa受试者的股骨软骨厚度（FCT）可以使用区域评分有效监测，具有不同的截止值。代码可从https://github.com/adaydar/Deep-OsteoCls获得

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引用次数: 0

Sympathetic nerve activity recovery from the skin recording using the modern optimal shrinkage technique 现代最佳收缩技术记录皮肤交感神经活动恢复

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-10 DOI: 10.1016/j.bspc.2026.109710

Pei-Chun Su , Chao-Yi Chen , Chia-Hao Kuo , Wei-Chung Tsai , Hau-Tieng Wu

Objective

The widely used bandpass filter (BPF)-based algorithm for recovering sympathetic nerve activity (SNA) from the skin sympathetic nerve activity (SKNA −I) signal, recorded via electrocardiogram electrodes or subcutaneous sympathetic nerve activity (SCNA-I) in a lead I setup, has limitations. It excludes spectral information outside the BPF range and may retain artifacts, such as cardiac activity or pacemaker interference, in the recovered SNA (rSNA) signal. This study aims to develop an algorithm that recovers the full spectral SNA information as comprehensively as possible for evaluating the autonomic nervous system (ANS).

Methods

We propose a novel algorithm, S3 (SNA from Shrink and Subtraction), which integrates the optimal shrinkage algorithm (eOptShrink) with the template subtraction (TS) method, and make the Matlab code publicly available. The performance of S3 was evaluated against other algorithms using semi-real simulated SKNA-I data, a human SKNA-I database including subjects with pacemakers or atrial fibrillation (Af), and a mice SCNA-I database.

Results

The S3 algorithm demonstrated numerical efficiency and outperformed existing approaches, including traditional TS, BPF and other methods, in both time and frequency domains. Notably, in addition to the traditional 500–1000 Hz spectral band, S3 effectively recovers spectral information across the 50–300 Hz and 300–500 Hz frequency bands. All quantitative results are supported by the rSNA tracing for visual inspections.

Conclusion

S3 overcomes key limitations of existing methods and accurately recovers full-spectrum SNA from human SKNA-I, including cases with pacemaker and AF, as well as from mouse SCNA-I, with both theoretical justification and numerical validation. Since S3 can recover spectral information across the 50–300 Hz and 300–500 Hz frequency bands, and ECG signals in the homecare environments are typically sampled at 1–2 kHz, S3 is potentially suitable for home-based ANS evaluation.

Significance

S3 enables exploration of the entire SNA spectrum and shows strong potential for ANS evaluation in homecare settings.

目的广泛使用的基于带通滤波器（BPF）的算法从皮肤交感神经活动（SKNA -I）信号中恢复交感神经活动（SNA），这些信号是通过心电图电极或在导联I装置中记录的皮下交感神经活动（SCNA-I），但存在局限性。它排除了BPF范围外的频谱信息，并可能保留伪象，如心脏活动或起搏器干扰，在恢复的SNA （rSNA）信号中。本研究旨在开发一种尽可能全面地恢复全谱SNA信息的算法，用于评估自主神经系统（ANS）。方法我们提出了一种新的算法S3 (SNA from Shrink and Subtraction)，它将最优收缩算法（eOptShrink）与模板减法（TS）方法相结合，并公开了Matlab代码。通过半真实的模拟SKNA-I数据、包括心脏起搏器或心房颤动（Af）受试者的人类SKNA-I数据库和小鼠SCNA-I数据库，对S3的性能与其他算法进行了评估。结果S3算法在时域和频域均优于传统TS、BPF等方法。值得注意的是，除了传统的500-1000 Hz频段外，S3还可以有效地恢复50-300 Hz和300-500 Hz频段的频谱信息。所有定量结果均由目视检查的rSNA追踪支持。结论s3克服了现有方法的主要局限性，准确地恢复了包括起搏器和心房颤动病例在内的人SCNA-I以及小鼠SCNA-I的全谱SNA，具有理论依据和数值验证。由于S3可以恢复50-300 Hz和300-500 Hz频段的频谱信息，并且家庭护理环境中的心电信号通常以1-2 kHz采样，因此S3可能适用于基于家庭的ANS评估。意义3能够探索整个SNA谱，并显示出在家庭护理环境中进行ANS评估的强大潜力。

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引用次数: 0

Spike sequences classification for dengue and Zika infections in mosquito neurons using deep pre-trained models 利用深度预训练模型对蚊子神经元中登革热和寨卡感染的刺突序列进行分类

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-09 DOI: 10.1016/j.bspc.2026.109748

Danial Sharifrazi , Nouman Javed , Roohallah Alizadehsani , Prasad N. Paradkar , U.Rajendra Acharya , Asim Bhatti

Mosquito-borne diseases are severe hazards to the health of both animals and humans. Aedes aegypti mosquitos are the primary vectors of several medically significant diseases, including dengue and Zika. Therefore, a thorough understanding of the neurons of mosquitos transmitting these diseases can be extremely beneficial in disease prevention. We hope to better comprehend the unique pattern found in considerable values of signal retrieved from mosquito neurons, known as spikes. There is currently no open-source neural spike sequence classification technique for mosquitos. To obtain outstanding outcomes, we demonstrate how to extract and classify spikes from mosquito neuron inputs using transfer learning approaches. Consequently, we highlight the role of deep pre-trained models that were trained using ImageNet weights.

The proposed methodology uses electrical spiking activity data from mosquito neurons collected with microelectrode array technology. To assess the method’s performance, data from 0, 1, 2, 3, and 7 days post-infection, reaching more than 15 million samples, were used. In this study, we also look at the influence of days post-infection on recognizing spikes in mosquito neurons.

Overall, we attempted for the first time to analyze the distinctive pattern in the spike sequence of mosquito neurons using Artificial Intelligence (AI) approaches and to determine the impact of these spikes over time.

蚊媒疾病是严重危害动物和人类健康的疾病。埃及伊蚊是几种医学上重要疾病的主要媒介，包括登革热和寨卡病毒。因此，彻底了解传播这些疾病的蚊子的神经元对预防疾病非常有益。我们希望更好地理解从蚊子神经元中获取的大量信号中发现的独特模式，即所谓的尖峰。目前还没有开源的蚊子神经脉冲序列分类技术。为了获得突出的结果，我们演示了如何使用迁移学习方法从蚊子神经元输入中提取和分类spike。因此，我们强调了使用ImageNet权重训练的深度预训练模型的作用。所提出的方法使用微电极阵列技术收集的蚊子神经元的电尖峰活动数据。为了评估该方法的性能，使用了感染后0、1、2、3和7天的数据，涉及超过1500万个样本。在这项研究中，我们还研究了感染后几天对蚊子神经元识别尖峰的影响。总体而言，我们首次尝试使用人工智能（AI）方法分析蚊子神经元尖峰序列的独特模式，并确定这些尖峰随时间的影响。

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引用次数: 0

DAAP-NET: Automatic identification and quantitative analysis of gastric wall structure for cancer screening using gastric ultrasound images DAAP-NET：利用胃超声图像对胃壁结构进行自动识别和定量分析，用于肿瘤筛查

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-09 DOI: 10.1016/j.bspc.2026.109648

Mustafain Rehman , Zhijun Liu , Miao Fan , Ahsan Humayun , Mingze Ding , Bin Liu

Objectives

Gastric cancer remains a significant public health challenge worldwide, ranked fifth in incidence and fourth in mortality among all malignant tumors. Early gastric cancer (EGC) detection is critical to improving survival rates.

Methods

We propose a new segmentation model, Dual Attention Atrous Pixel Network (DAAP-Net), designed to predict the gastric wall and stomach cavity in gastric ultrasound images. Post-segmentation, the predicted gastric wall mask extracts a region of interest (ROI) focused on the five-layer wall structure. The ROI undergoes a spatially diffused iterative enhancement (SDIE) technique to suppress intra-layer noise while preserving inter-layer transitions. We apply edge detection to the SDIE-refined ROI and compute layer thickness as pixel distances between successive edges, and normalize them into the proportion vector

x

. A scalar deviation

d

from the normal baseline quantifies abnormality.

Results

DAAP-Net outperforms state-of-the-art segmentation methods, achieving Intersection over Union scores of 0.7720 ± 0.0618 for normal gastric wall, 0.9007 ± 0.0495 for normal stomach cavity, 0.7607 ± 0.0780 for cancer gastric wall, and 0.8843 ± 0.0561 for cancer stomach cavity. Quantitative analysis shows gastric wall layer parameters differ markedly; the edge-derived deviation metric

d

separates cohorts, with normal mean 0.128 and cancer mean 0.508.

Conclusions

Our research highlights structural differences between normal and cancerous gastric walls, providing a reliable and non-invasive method for EGC detection. Current limitations include manual ROI selection and occasional errors in low-contrast regions. Future work includes automated ROI selection, adding a benign-labeled cohort, a multi-center dataset, and improving model accuracy for real-time clinical applications.

目的胃癌仍然是全球重大的公共卫生挑战，在所有恶性肿瘤中发病率排名第五，死亡率排名第四。早期胃癌（EGC）检测是提高生存率的关键。方法提出了一种新的分割模型——双注意像素网络（DAAP-Net），用于预测胃超声图像中的胃壁和胃腔。分割后，预测的胃壁掩膜提取一个关注五层胃壁结构的感兴趣区域（ROI）。ROI采用空间扩散迭代增强（SDIE）技术来抑制层内噪声，同时保持层间过渡。我们将边缘检测应用于sdie细化的ROI，并将层厚度作为连续边缘之间的像素距离计算，并将其归一化为比例向量x。从正常基线的标量偏差d量化异常。ResultsDAAP-Net优于最先进的分割方法,实现十字路口在联盟得分为0.7720±0.0618正常胃壁,0.9007±0.0495正常胃腔,为癌症胃壁0.7607±0.0780,0.8843±0.0561对癌症的胃腔。定量分析显示胃壁各层参数差异明显；边缘衍生偏差度量d分隔队列，正常平均值为0.128，癌症平均值为0.508。结论sour研究突出了正常和癌变胃壁的结构差异，为EGC的检测提供了可靠、无创的方法。目前的限制包括手动ROI选择和在低对比度区域偶尔出现错误。未来的工作包括自动化ROI选择，添加良性标记队列，多中心数据集，以及提高实时临床应用的模型准确性。

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引用次数: 0

Cortical network dynamics and neural decoding of fine motor complexity via fNIRS and attention-based deep learning 基于fNIRS和基于注意的深度学习的皮层网络动力学和精细运动复杂性的神经解码

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-09 DOI: 10.1016/j.bspc.2026.109758

Xing Ji , Zhong Yin , Yifei Bi , Kaiwei Yu , Yize Li , Jiafa Chen , Dawei Zhang

Fine motor decline serves as a critical early biomarker for neurodegenerative diseases like Parkinson’s disease, making its accurate assessment essential for early detection and intervention. While functional near-infrared spectroscopy (fNIRS) offers a portable, non-invasive neuroimaging solution, the precise cortical dynamics underlying varying levels of motor complexity remain underexplored. This study aims to investigate how fine motor task complexity modulates cortical activation and functional network topology. A secondary objective is to develop and validate a high-performance deep learning model to classify motor complexity levels from fNIRS signals. fNIRS data were recorded from healthy participants performing five fine-motor tasks of increasing complexity, and activation analyses were combined with graph-theoretical metrics to characterize neurophysiological responses. To classify the complexity of fine motor tasks from fNIRS signals, this study developed a bidirectional long short-term memory (Bi-LSTM) model. Performance evaluation used leave-one-out cross-validation, supplemented by multi-seed training to improve robustness. The model achieved an average classification accuracy of 90.67% ± 7.07% (95% CI: ± 2.68%) and an AUC of 0.9720 ± 0.0431, outperforming traditional support vector machine (by 21.3%) and Bi-LSTM (by 10.97%). These results demonstrate the model’s strong generalization across subjects and its ability to capture temporal-spatial patterns of cortical activation associated with increasing task complexity, providing a promising foundation for fine motor decoding and adaptive neurorehabilitation.

精细运动衰退是帕金森病等神经退行性疾病的重要早期生物标志物，对其进行准确评估对于早期发现和干预至关重要。虽然功能性近红外光谱（fNIRS）提供了一种便携式、非侵入性的神经成像解决方案，但不同运动复杂性水平下的精确皮层动力学仍未得到充分探索。本研究旨在探讨精细运动任务复杂性如何调节皮层激活和功能网络拓扑结构。第二个目标是开发和验证高性能深度学习模型，以从近红外光谱信号中分类运动复杂性水平。研究人员记录了健康参与者执行五项复杂精细运动任务时的fNIRS数据，并将激活分析与图形理论指标相结合，以表征神经生理反应。为了从近红外信号中对精细运动任务的复杂性进行分类，本研究建立了双向长短期记忆（Bi-LSTM）模型。性能评估采用留一交叉验证，辅以多种子训练来提高鲁棒性。该模型的平均分类准确率为90.67%±7.07% (95% CI：±2.68%)，AUC为0.9720±0.0431，优于传统的支持向量机（21.3%）和Bi-LSTM（10.97%）。这些结果表明，该模型具有很强的通用性，并且能够捕捉到与任务复杂性增加相关的皮层激活的时空模式，为精细运动解码和适应性神经康复提供了有希望的基础。

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引用次数: 0

IoT based air quality monitoring and asthma alerts driven by non-crossing quantile regression neural networks 基于物联网的空气质量监测和哮喘警报，由非交叉分位数回归神经网络驱动

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-09 DOI: 10.1016/j.bspc.2026.109565

Abhijit Das , B.M. Chandrakala , N Shobha , J. Reshma , Vikranth Bhoothpur , Rakesh Kumar Godi

Asthma is a chronic respiratory disease that remains difficult to manage due to variable symptoms and diverse environmental triggers. Conventional monitoring approaches often rely on costly equipment and subjective self-reports, limiting timely interventions. Moreover, existing deep learning models suffer from issues like limited data quality, poor handling of outliers and lack of accurate risk assessment. To overcome these complications, IoT Based Air Quality Monitoring and Asthma Alerts Driven by Non-Crossing Quantile Regression Neural Networks (AM-IoT-NCQRNN) is proposed. Initially, the data is collected from Air Quality and Health Impact Dataset. Then the input data is preprocessed under Robust Maximum Correntropy Kalman Filter (RMCKF) to handle missing elements, noise and outliers. RMCKF is for its correntropy-based similarity, offering superior outlier suppression compared to median filters, low-rank imputation and standard Kalman filtering. Afterwards, the preprocessed data is given to the Non-Crossing Quantile Regression Neural Network (NCQRNN) which predicts and classifies health impact scores of asthma as very high, high, moderate, very low and low. NCQRNN applies a non-crossing quantile constraint, ensuring stable and interpretable risk estimation compared to Regression Neural Networks (RNNs) that yield inconsistent boundaries under fluctuating inputs. The proposed approach is implemented as a smartphone application, with real-time data collected through an IoT-based system using a Raspberry Pi and estimated using metrics such as accuracy, precision, recall, f1-score, specificity, ROC and computational time. Finally, the performance of proposed AM-IoT-NCQRNN method attains 19.76%, 24.00% and 19.07% higher accuracy and 29.56%, 24.22% and 28.57% higher precision when compared with existing methods.

哮喘是一种慢性呼吸道疾病，由于各种症状和各种环境触发因素，仍然难以控制。传统的监测方法往往依赖于昂贵的设备和主观的自我报告，限制了及时的干预。此外，现有的深度学习模型还存在数据质量有限、异常值处理差、缺乏准确的风险评估等问题。为了克服这些复杂性，提出了基于物联网的非交叉分位数回归神经网络（AM-IoT-NCQRNN）驱动的空气质量监测和哮喘警报。最初，数据是从空气质量和健康影响数据集收集的。然后在鲁棒最大相关卡尔曼滤波（RMCKF）下对输入数据进行预处理，处理缺失元素、噪声和异常值。RMCKF是基于相关系数的相似性，与中值滤波器、低秩插值和标准卡尔曼滤波相比，它提供了更好的离群值抑制。然后，将预处理后的数据输入到非交叉分位数回归神经网络（NCQRNN）中，对哮喘的健康影响评分进行预测和分类，分为非常高、高、中等、非常低和低。NCQRNN采用非交叉分位数约束，与在波动输入下产生不一致边界的回归神经网络（rnn）相比，确保了稳定和可解释的风险估计。所提出的方法是作为智能手机应用程序实现的，通过使用树莓派的基于物联网的系统收集实时数据，并使用准确度、精密度、召回率、f1评分、特异性、ROC和计算时间等指标进行估计。最后，与现有方法相比，本文提出的AM-IoT-NCQRNN方法的准确率分别提高了19.76%、24.00%和19.07%，精度分别提高了29.56%、24.22%和28.57%。

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引用次数: 0