Biomedical Signal Processing and Control最新文献_第5页

Parkinson’s disease detection from resting-state EEG using a Transformer model with Multi-Head Attention explanations 基于多头注意解释的变压器模型的静息状态脑电图帕金森病检测

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.2026.109811

Valeriana Mancazzo , Elena Sibilano , Domenico Buongiorno, Raffaele Carli, Vitoantonio Bevilacqua, Antonio Brunetti

Despite the proven potential of using Deep Learning (DL) models based on electroencephalographic (EEG) signals to detect neurological disorders like Parkinson’s Disease (PD), their adoption in clinical practice is limited due to insufficient reliability and generalizability. We propose an interpretable end-to-end DL framework leveraging the Multi-Head Attention (MHA) component of Transformers to classify EEG signals of 100 PD patients and 79 control subjects across three public resting-state EEG datasets. A systematic interpretability approach, including embedding visualization and MHA-based temporal and spectral analysis through statistical tests, is proposed to enhance the identification of discriminative biomarkers. Experimental findings across a large, multi-centric cohort of subjects demonstrated the framework’s capability to detect meaningful EEG patterns in the frequency intervals of interest.

Interpretability analysis revealed that MHA focused on specific temporal patches in the input signal, which correlated to the classification outcomes. Spectral analysis identified significant power differences in Theta and Beta bands, capturing neural patterns of cognitive and motor dysfunction in PD. Furthermore, attention-guided segmentation improved the sensitivity of spectral biomarkers, such as Alpha/Theta ratio and Beta relative power, consistent with prior literature. Moreover, the proposed approach yielded the highest epoch-level mean AUCs of

0.91 \pm 0.01

on Theta,

0.84 \pm 0.03

on Alpha, and

0.81 \pm 0.01

on All-band, achieving state-of-the-art performances while also demonstrating robustness to heterogeneous data.

尽管使用基于脑电图（EEG）信号的深度学习（DL）模型来检测帕金森病（PD）等神经系统疾病已被证明具有潜力，但由于可靠性和通用性不足，它们在临床实践中的应用受到限制。我们提出了一个可解释的端到端深度学习框架，利用变压器的多头注意（MHA）组件对100名PD患者和79名对照受试者的EEG信号进行分类，这些数据来自三个公共静息状态EEG数据集。提出了一种系统的可解释性方法，包括嵌入可视化和基于mha的时间和光谱分析，通过统计测试来提高鉴别生物标志物的识别能力。在一个大型的、多中心的受试者队列中，实验结果证明了该框架在感兴趣的频率间隔中检测有意义的脑电图模式的能力。可解释性分析表明，MHA集中于输入信号中特定的时间斑块，这与分类结果相关。频谱分析发现Theta和Beta波段的显著功率差异，捕捉PD患者认知和运动功能障碍的神经模式。此外，注意引导分割提高了光谱生物标志物的灵敏度，如Alpha/Theta比和Beta相对功率，与先前的文献一致。此外，该方法在Theta上的平均auc最高为0.91±0.01，Alpha上为0.84±0.03，All-band上为0.81±0.01，达到了最先进的性能，同时也证明了对异构数据的鲁棒性。

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引用次数: 0

Normalized mutual information centrality-based BCI channels selecting enhanced by refined multiple frequency bands 基于归一化互信息中心性的BCI信道选择，改进了多个频段

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.2026.109734

Yu Wang , Guorui Li , Xin Zhang , Shengpu Xu , Bo Yao , Jiangbo Pu

Brain-computer interface (BCI) applications are significantly influenced by electroencephalography (EEG) signal acquisition. The requirement for numerous channels not only increases the preparation time but also introduces redundant information, which negatively impacts BCI performance. To address this issue, a multi-frequency channel selection method, termed Multi-Frequency-normalized Mutual Information and Betweenness Centrality-based Channel Selection (MF-MIBCCS), was proposed. This approach independently selected optimal channels across multiple frequency bands to effectively mitigate cross-frequency interference. To evaluate the proposed method, studies were conducted on two datasets (BCI Competition III-IIIa and a self-collected dataset). The results demonstrated that the MF-MIBCCS outperformed conventional all-channel usage, achieving superior classification performance with fewer than 20 channels on average. The MF-MIBCCS method shows significant potential for reducing the number of required EEG channels, thereby facilitating more efficient and personalized BCI system design.

脑机接口（BCI）的应用受到脑电图信号采集的显著影响。对众多通道的需求不仅增加了准备时间，而且引入了冗余信息，对BCI性能产生了负面影响。为了解决这一问题，提出了一种基于多频率归一化互信息和中间度中心性的多频率信道选择方法（MF-MIBCCS）。该方法在多个频带中独立选择最优信道，有效地减轻了交叉频率干扰。为了评估所提出的方法，研究人员在两个数据集（BCI Competition III-IIIa和一个自收集数据集）上进行了研究。结果表明，MF-MIBCCS优于传统的全通道使用，在平均少于20个通道的情况下实现了优越的分类性能。MF-MIBCCS方法在减少所需脑电通道数量方面显示出巨大的潜力，从而促进更高效和个性化的脑机接口系统设计。

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引用次数: 0

Design, modeling, and experimental evaluation of a fuzzy-controlled 2DOF knee exoskeleton for gait rehabilitation 用于步态康复的模糊控制二自由度膝关节外骨骼的设计、建模和实验评估

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.2026.109730

Mohammad Amin Iravani Rad , Ali Mokhtarian , Mohammad Taghi Karimi , Davood Toghraie

Wearable exoskeletons, equipped with intelligent control algorithms, have emerged as promising tools for restoring mobility in patients with neuromuscular impairments. This study presents the modeling, simulation, and experimental validation of a two-degree-of-freedom (2DOF) knee exoskeleton, specifically designed for adaptive gait rehabilitation. A dynamic link-segment model of the swing phase is developed using Lagrangian formulation, allowing for accurate estimation of knee torque (∼25 Nm) and power demands (>100 W), which guide actuator selection. A fuzzy logic controller (FLC) is designed and compared with a conventional PID controller to regulate knee joint motion. Unlike standard encoder-based setups, this system employs a potentiometer as an angular sensor, offering a low-cost yet effective solution for joint angle tracking. The exoskeleton also features a conical gearbox and mechanical joint limits (5°–55°) to ensure structural compactness and biomechanical safety. Experimental validation was conducted on six healthy subjects across three gait conditions (natural, PID-assisted, and FLC-assisted) using optical motion capture and kinematic analysis. Results show that the fuzzy controller achieved smoother transitions and reduced trajectory error (RMS error reduced by ∼40%) compared to PID control, closely approximating natural knee motion. These findings highlight the feasibility of signal-driven, adaptive control frameworks for future clinical applications in personalized neurorehabilitation.

配备智能控制算法的可穿戴外骨骼已经成为恢复神经肌肉损伤患者活动能力的有前途的工具。本研究介绍了专门为适应性步态康复设计的二自由度（2DOF）膝关节外骨骼的建模、仿真和实验验证。使用拉格朗日公式开发了摆动相位的动态连杆段模型，可以准确估计膝关节扭矩（~ 25 Nm）和功率需求（>100 W），从而指导执行器的选择。设计了一种模糊控制器（FLC），并与传统的PID控制器进行了比较。与标准的基于编码器的设置不同，该系统采用电位器作为角度传感器，为关节角度跟踪提供了低成本但有效的解决方案。外骨骼还具有锥形齿轮箱和机械关节极限（5°-55°），以确保结构紧凑性和生物力学安全性。通过光学运动捕捉和运动学分析，对六名健康受试者进行了三种步态状态（自然、pid辅助和flc辅助）的实验验证。结果表明，与PID控制相比，模糊控制器实现了更平滑的过渡，减少了轨迹误差（RMS误差减少了约40%），非常接近膝关节的自然运动。这些发现强调了信号驱动、自适应控制框架在个性化神经康复中未来临床应用的可行性。

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引用次数: 0

Synthetic histopathology with controllable class distribution: A dual-GAN framework for melanoma segmentation 具有可控类分布的合成组织病理学：黑色素瘤分割的双gan框架

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.2026.109741

Ziad Elshaer , Ahmed Jamal , Essam A. Rashed

Tumor-infiltrating lymphocytes (TILs) assessment in melanoma histopathology is critical for predicting immunotherapy response and improving patient outcomes, yet current automated segmentation methods are severely constrained by limited datasets and pronounced class imbalance. We present a novel dual-generator adversarial framework that revolutionizes synthetic histopathology data generation by decomposing the complex synthesis problem into two specialized sequential tasks: controllable mask generation with user-specified class distributions, followed by high-fidelity histopathology image synthesis. This innovative approach enables precise dataset augmentation with any desired number of tissue classes per image, fundamentally addressing the scarcity of balanced training data. Leveraging the PUMA Grand Challenge dataset, we systematically generated two complementary datasets and evaluated them using a custom U-Net architecture that integrates a powerful MedSAM encoder with a specialized decoder optimized for fine-grained tissue segmentation. Our dual-GAN framework demonstrates exceptional capability in generating photorealistic histopathology images while maintaining precise control over tissue class distributions and spatial relationships. The proposed architecture achieved outstanding performance with an F1 score of 0.91 on the PUMA dataset and new data from the three-class per-image dataset, significantly advancing the state-of-the-art in melanoma tissue segmentation. This scalable framework establishes a new paradigm for computational pathology, enabling robust TIL assessment and enhanced clinical decision-making in melanoma management.

黑色素瘤组织病理学中肿瘤浸润淋巴细胞（til）的评估对于预测免疫治疗反应和改善患者预后至关重要，但目前的自动分割方法受到有限的数据集和明显的分类不平衡的严重限制。我们提出了一种新的双生成器对抗框架，通过将复杂的合成问题分解为两个专门的顺序任务，彻底改变了合成组织病理学数据的生成：具有用户指定类分布的可控掩膜生成，然后是高保真的组织病理学图像合成。这种创新的方法可以通过每张图像的任意数量的组织类来精确地增强数据集，从根本上解决了平衡训练数据的稀缺性。利用PUMA Grand Challenge数据集，我们系统地生成了两个互补的数据集，并使用定制的U-Net架构对它们进行了评估，该架构集成了强大的MedSAM编码器和针对细粒度组织分割优化的专用解码器。我们的双gan框架在生成逼真的组织病理学图像同时保持对组织类分布和空间关系的精确控制方面表现出卓越的能力。该架构在PUMA数据集和来自每幅图像三级数据集的新数据上取得了0.91的F1分数，显著推进了黑色素瘤组织分割的最新技术。这个可扩展的框架为计算病理学建立了一个新的范例，使TIL评估和增强黑色素瘤管理的临床决策成为可能。

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引用次数: 0

Accuracy enhancement in melanoma diagnosis: A comparative study of residual networks and visual geometry group architectures 提高黑色素瘤诊断的准确性：残差网络和视觉几何群结构的比较研究

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.2026.109635

Shisheng Chen , Wenjing Pan , Tongyao Chen , Xinxin Xie , Yi Zhang

Melanoma is a highly aggressive skin malignancy, and its early detection is critical for reducing mortality. With the rapid progress of deep learning in medical imaging, convolutional neural networks (CNNs) have become powerful tools for automated pathological image analysis. This study aimed to systematically evaluate the performance, interpretability, and potential clinical utility of different deep learning models in classifying melanoma on H&E-stained pathology images. A total of 312 clinical H&E whole-slide images (210 normal skin and 102 melanoma) were acquired and preprocessed through resizing, normalization, and data augmentation. Four CNN architectures—ResNet, VGG, MobileNetV2, and DenseNet121—were constructed for classification, and five-fold cross-validation was used for performance evaluation based on accuracy, sensitivity, specificity, F1-score, and AUC. Grad-CAM was further applied for model interpretability, with pathological verification by experienced dermatopathologists. All four models successfully differentiated melanoma from normal skin tissue, with ResNet achieving the highest mean accuracy (96.10%) and the best F1-score and AUC. VGG exhibited strong stability, while MobileNetV2 and DenseNet121 provided higher computational efficiency but slightly lower diagnostic performance. Statistical analysis confirmed that ResNet outperformed the other models significantly (p < 0.05). Grad-CAM visualization demonstrated that the highlighted regions corresponded closely to key histopathological features of melanoma, indicating that the model’s decision-making process is pathologically plausible.

黑色素瘤是一种高度侵袭性的皮肤恶性肿瘤，早期发现对降低死亡率至关重要。随着深度学习在医学成像领域的快速发展，卷积神经网络（cnn）已经成为病理图像自动分析的有力工具。本研究旨在系统评估不同深度学习模型在H&； e染色病理图像上对黑色素瘤进行分类的性能、可解释性和潜在的临床应用。共获得312张临床H&；E全片图像（210张正常皮肤和102张黑色素瘤），并通过调整大小、归一化和数据增强进行预处理。构建了4种CNN架构（resnet、VGG、MobileNetV2和densenet121）进行分类，并基于准确性、灵敏度、特异性、f1评分和AUC进行了5次交叉验证。Grad-CAM进一步应用于模型的可解释性，由经验丰富的皮肤病理学家进行病理验证。四种模型均成功将黑色素瘤与正常皮肤组织区分开来，其中ResNet的平均准确率最高（96.10%），f1评分和AUC最高。VGG表现出很强的稳定性，而MobileNetV2和DenseNet121提供了更高的计算效率，但诊断性能略低。统计分析证实，ResNet的表现明显优于其他模型（p < 0.05）。Grad-CAM可视化显示，突出显示的区域与黑色素瘤的关键组织病理学特征密切相关，表明该模型的决策过程在病理学上是合理的。

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引用次数: 0

Population information exchange differential evolution with CMAES for multi-threshold segmentation of breast cancer pathologic images 群体信息交换差分进化与CMAES在乳腺癌病理图像多阈值分割中的应用

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.2026.109796

Zhi Liu, Weibin Chen, Huiling Chen

The critical role of accurate segmentation in breast cancer diagnosis motivates this research. We propose a novel approach that integrates an advanced optimization algorithm to significantly improve the segmentation precision of pathological images. Specifically, we first improve the Differential Evolution (DE) algorithm by incorporating the Population Information Exchange (PIE) strategy and the Covariance Matrix Adaptation Evolution Strategy (CMAES) to propose TDE, The PIE mechanism improves the global search ability of the algorithm through random population information exchange, effectively suppressing premature convergence. The CMAES strategy generates high-quality solutions based on covariance learning, significantly improving the accuracy of local exploitation. Subsequently, TDE is coupled with Rényi entropy, forming the core of our segmentation methodology for breast histopathology images. We initially verified the optimization capability of TDE on the IEEE CEC 2017 functions set. Statistical validations through the Wilcoxon signed-rank test and Friedman test confirmed the superiority of TDE. Following this, we evaluated the multi-threshold segmentation model combining TDE and Rényi entropy on 9 breast cancer pathology images, demonstrating that the TDE algorithm outperforms peer algorithms. The comprehensive experimental results affirm TDE’s outstanding performance in both optimization benchmarks and medical image segmentation applications.

准确分割在乳腺癌诊断中的关键作用激发了这项研究。我们提出了一种集成先进优化算法的新方法，以显着提高病理图像的分割精度。具体而言，我们首先将种群信息交换（PIE）策略与协方差矩阵适应进化策略（CMAES）相结合，对差分进化（DE）算法进行改进，提出了差分进化（DE）算法，PIE机制通过随机种群信息交换提高了算法的全局搜索能力，有效抑制了过早收敛。CMAES策略基于协方差学习生成高质量的解，显著提高了局部开发的准确性。随后，将TDE与rsamnyi熵相结合，形成乳腺组织病理学图像分割方法的核心。我们在IEEE CEC 2017函数集上初步验证了TDE的优化能力。通过Wilcoxon sign -rank检验和Friedman检验的统计验证证实了TDE的优越性。在此基础上，我们对9张乳腺癌病理图像进行了TDE和rsamnyi熵相结合的多阈值分割模型的评估，结果表明TDE算法优于同类算法。综合实验结果证实了TDE在优化基准测试和医学图像分割应用中的出色表现。

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引用次数: 0

Neural synchrony and attention dynamics during naturalistic video viewing: a gender comparison using EEG and deep learning approaches 自然视频观看过程中的神经同步和注意动力学：使用脑电图和深度学习方法的性别比较

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.2026.109763

Tengis Tserendondog , Bat-Erdene Gotov , Uurtsaikh Luvsansambuu , Dong-Sung Pae , Hansaem Park

This study introduces a hybrid electroencephalographic (EEG) framework that integrates classical analyses with an interpretable attention-driven deep learning model to examine gender-related neural differences during naturalistic video viewing. Seventy-four adults (37 males, 37 females) watched a 117-second silent video while 14-channel EEG signals were recorded. Classical measures, including inter-subject correlation (ISC) and wavelet-based time–frequency mapping, revealed that males exhibited significantly higher ISC than females (0.065 ± 0.040 vs. 0.031 ± 0.033; t(72) = 3.88, p = 0.0002, Cohen’s d = 0.91), indicating stronger inter-brain synchrony during rapid scene transitions. Spectral analysis further demonstrated stronger frontal beta power in males, associated with top-down control, and enhanced parietal alpha activity in females, linked to sensory integration. To move beyond aggregated metrics, a hierarchical attention-based autoencoder was employed to reconstruct EEG signals while assigning time-resolved attention weights. Temporal attention profiles were computed in 4-second windows and compared across genders using Welch’s t-tests with FDR correction. Six windows (28, 32, 36, 60, 76, 80 s) showed significant group differences (q < 0.05), demonstrating dynamic gender-specific attentional shifts. These divergences aligned with ISC and spectral peaks, indicating that the deep-learning model captured the same engagement-relevant segments identified by classical metrics, but with higher temporal precision. By linking physiological markers (ISC and frequency dynamics) with interpretable temporal salience, the framework provides a coherent multi-scale account of how males and females differentially process continuous visual stimuli. The approach advances methodological transparency in EEG-based deep learning and supports applications in personalized media design, adaptive learning environments, neuromarketing, and gender-aware brain–computer interfaces.

本研究引入了一种混合脑电图（EEG）框架，该框架将经典分析与可解释的注意力驱动深度学习模型相结合，以研究自然视频观看过程中与性别相关的神经差异。74名成年人（37名男性，37名女性）观看了一段117秒的无声视频，同时记录了14个通道的脑电图信号。经典测量方法包括受试者间相关（ISC）和基于小波的时频映射，结果显示男性的ISC显著高于女性（0.065±0.040 vs 0.031±0.033;t(72) = 3.88, p = 0.0002, Cohen’s d = 0.91），表明在快速场景转换过程中大脑间同步更强。光谱分析进一步表明，男性的额叶β能量更强，与自上而下的控制有关，而女性的顶叶α活性增强，与感觉整合有关。为了超越聚合指标，在分配时间分辨注意力权重的同时，采用基于分层注意的自编码器重构脑电信号。在4秒窗口内计算时间注意概况，并使用带有FDR校正的Welch’s t检验比较性别间的差异。6个窗口（28、32、36、60、76、80 s）组间差异显著（q < 0.05），显示出动态的性别注意转移。这些差异与ISC和光谱峰一致，表明深度学习模型捕获了与经典指标识别的相同的参与相关片段，但具有更高的时间精度。通过将生理标记（ISC和频率动态）与可解释的时间显著性联系起来，该框架提供了一个连贯的多尺度说明男性和女性如何不同地处理连续视觉刺激。该方法提高了基于脑电图的深度学习方法的透明度，并支持在个性化媒体设计、自适应学习环境、神经营销和性别意识脑机接口中的应用。

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引用次数: 0

Design of an optimized rotation-invariant coordinate convolutional neural network driven medical IoT recommendation system integrating sentiment analysis for improved patient preference prediction 设计一种优化的旋转不变坐标卷积神经网络驱动的医疗物联网推荐系统，集成情感分析，改进患者偏好预测

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-08 DOI: 10.1016/j.bspc.2026.109742

Rethina Kumar B , P. Sudhakaran , M. Baritha Begum , S. Rajeswari

Chronic and lifestyle-related diseases are rising globally, creating significant societal and economic burdens. To support effective long-term patient monitoring, an Optimized Rotation-Invariant Coordinate Convolutional Neural Network-driven Medical IoT Recommendation System integrating Sentiment Analysis for Improved Patient Preference Prediction (RICNN-IoT-SA-IPP) is proposed. The system collects multimodal data, including physiological and behavioural signals from IoT-based healthcare sensors and combines it with patient feedback sourced from electronic health records and medical consultation platforms. A Fast Guided Median Filter (FGMF) is employed to denoise and normalize the input, followed by spatial feature extraction utilizing Synchro-Transient-Extracting Transform (STET). These features are analyzed through a Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDSGAN) to infer patient sentiment. A Rotation-Invariant Coordinate Convolutional Neural Network (RICNN) then performs preference prediction. To enhance prediction accuracy, the Levy Pelican Optimization Algorithm (LPOA) is used for optimizing feature weights and model parameters. The system performance is evaluated using Accuracy, Precision, Recall, F1-Score, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Computational Time. The proposed RICNN-IoT-SA-IPP model achieved 99.32% accuracy and 98.34% precision, while maintaining low error rates with MAE = 0.0855 and MSE = 0.0864, respectively. When compared with existing models, these outcomes represent an improvement of approximately 3–5% in classification metrics and a significant reduction in prediction error. This demonstrates that the proposed framework provides highly accurate, reliable, and computationally efficient patient preference predictions.

慢性病和与生活方式有关的疾病正在全球上升，造成重大的社会和经济负担。为了支持有效的长期患者监测，提出了一种优化的旋转不变坐标卷积神经网络驱动的医疗物联网推荐系统，该系统集成了改进患者偏好预测的情感分析（RICNN-IoT-SA-IPP）。该系统收集多模式数据，包括来自基于物联网的医疗传感器的生理和行为信号，并将其与来自电子健康记录和医疗咨询平台的患者反馈相结合。采用快速引导中值滤波（FGMF）对输入进行去噪和归一化处理，然后利用同步瞬态提取变换（STET）进行空间特征提取。这些特征通过多模态对比域共享生成对抗网络（MCDSGAN）进行分析，以推断患者的情绪。然后使用旋转不变坐标卷积神经网络（RICNN）进行偏好预测。为了提高预测精度，采用Levy Pelican Optimization Algorithm （LPOA）对特征权值和模型参数进行优化。系统性能评估使用准确性，精密度，召回率，F1-Score，平均绝对误差（MAE），均方误差（MSE）和计算时间。所提出的RICNN-IoT-SA-IPP模型准确率为99.32%，精度为98.34%，同时保持较低的错误率，MAE = 0.0855, MSE = 0.0864。与现有模型相比，这些结果在分类指标上提高了约3-5%，并显著降低了预测误差。这表明所提出的框架提供了高度准确、可靠和计算效率高的患者偏好预测。

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引用次数: 0

CNN–LSTM based deep learning approach for remote photoplethysmography and cardiac activity monitoring leveraging minimal data 基于CNN-LSTM的深度学习方法，利用最小数据进行远程光容积脉搏波和心脏活动监测

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-07 DOI: 10.1016/j.bspc.2026.109787

Saran Zeb , Xiaocong Lian , Wajid Mumtaz , Kegang Wang

Precise heart rate measurement is crucial for assessing an individual’s health, offering valuable insights into their heart condition and cardiovascular activity. Remote photoplethysmography (rPPG) provides a contactless technique for measuring heart rate and monitoring cardiac activity without direct skin contact. This method is especially advantageous in scenarios where direct contact is not feasible or desirable, such as during pandemics to avoid infection risk. Despite significant progress, rPPG still faces challenges, including variations in illumination, motion artifacts, sensor noise, and skin tone variations, which complicate accurate waveform capture. This study proposes a deep neural network model comprising convolutional and LSTM layers to accurately detect the blood volume pulse (BVP) and monitor cardiac activity from RGB and NIR video frames. Notably, the model was trained on single-subject, single-channel data, effectively predicting the PPG waveform and estimating heart rate. Furthermore, the model was trained and tested on videos from four different devices—a webcam, smartphone, RealSense color camera, and NIR camera, ensuring robustness across various sensor types. The model was evaluated on four publicly available datasets—VIPL-HR, MR-NIRP, UBFC-rPPG, and MPSC-rPPG—achieving a mean absolute error (MAE) of 2.64 beats per minute for the same subject and 4.84 beats per minute for different subjects. Achieving robust and accurate heart rate estimation from a single subject across various sensors and challenging scenarios underscores the potential of this contactless model for cardiovascular assessment.

精确的心率测量对于评估个人健康状况至关重要，为了解他们的心脏状况和心血管活动提供了有价值的见解。远程光电容积脉搏波描记（rPPG）提供了一种非接触式技术来测量心率和监测心脏活动，而无需直接接触皮肤。这种方法在不可行或不需要直接接触的情况下特别有利，例如在大流行期间，以避免感染风险。尽管取得了重大进展，但rPPG仍然面临挑战，包括光照变化、运动伪影、传感器噪声和肤色变化，这些都会使准确的波形捕获复杂化。本研究提出了一种包含卷积层和LSTM层的深度神经网络模型，用于准确检测RGB和NIR视频帧的血容量脉冲（BVP）并监测心脏活动。值得注意的是，该模型是在单受试者、单通道数据上训练的，有效地预测了PPG波形并估计了心率。此外，该模型在四种不同设备（网络摄像头、智能手机、RealSense彩色相机和近红外相机）的视频上进行了训练和测试，以确保在各种传感器类型上的稳健性。该模型在四个公开可用的数据集（vipl - hr、MR-NIRP、UBFC-rPPG和mpsc - rppg）上进行了评估，同一受试者的平均绝对误差（MAE）为每分钟2.64次，不同受试者的平均绝对误差为每分钟4.84次。通过各种传感器和具有挑战性的场景，从单个受试者获得稳健和准确的心率估计，强调了这种非接触式心血管评估模型的潜力。

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引用次数: 0

Federated learning for brain tumour detection and classification using improved ShuffleNet-PCNN architecture 基于改进ShuffleNet-PCNN架构的脑肿瘤检测和分类的联邦学习

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Biomedical Signal Processing and Control

Pub Date : 2026-02-07 DOI: 10.1016/j.bspc.2026.109719

A.Ramesh Khanna, P. Thilakavathy

Brain tumors are a major worldwide health concern, which emphasizes the significance of prompt and accurate diagnosis for efficient treatment planning and management. Differentiating tumors from normal tissues is essential when assessing medical imaging. However, there are a number of challenges with traditional medical imaging techniques for brain tumor detection. Limited datasets, regulations on privacy limiting data sharing, and the requirement for specialized knowledge to correctly analyze medical images could all be obstacles to current approaches. A novel approach called Federated Learning with SNet-PC for Brain Tumor Detection and Classification (FL-SNet-PC) is proposed. This approach utilizes Federated Learning, which incorporates LP-pooled layer-assisted ShuffleNet (LP-SNet) and Parallel Convolutional Neural Network (PCNN) models. During local training, the SNet-PC method is used, which combines the LP-SNet and PCNN architectures. The local training pipeline has several stages, such as preprocessing, segmentation, and feature extraction. Initially, the input image undergoes preprocessing using the Wiener filtering technique to normalize the image. Then, precise segmentation is achieved using the Yeo-Johnson-based Balanced Iterative Reducing and Clustering Using Hierarchies (YJ-BIRCH) algorithm. After segmentation, feature extraction is done, where shape features, Grey-Level Co-Occurrence Matrix (GLCM) features, and Sobel Gradient-based Pyramid Histogram of Gradient Orientation (SG-PHOG) are captured from the segmented image. Once the local training process is completed, they are then sent to a central server for global aggregation. Finally, the global training process aids in detecting and classifying brain tumors effectively.

脑肿瘤是一个主要的全球健康问题，这强调了及时和准确的诊断对有效的治疗计划和管理的重要性。在评估医学影像时，将肿瘤与正常组织区分开来至关重要。然而，传统的医学成像技术在脑肿瘤检测方面存在许多挑战。有限的数据集、限制数据共享的隐私法规，以及对正确分析医学图像的专业知识的要求，都可能成为当前方法的障碍。提出了一种基于SNet-PC的联邦学习脑肿瘤检测与分类方法。这种方法利用了联邦学习，结合了lp池层辅助ShuffleNet （LP-SNet）和并行卷积神经网络（PCNN）模型。在局部训练中，采用了结合LP-SNet和PCNN架构的SNet-PC方法。局部训练管道包括预处理、分割和特征提取等几个阶段。首先，使用维纳滤波技术对输入图像进行预处理，使图像归一化。然后，使用基于yeo - johnson的平衡迭代约简和分层聚类（YJ-BIRCH）算法实现精确分割。分割后进行特征提取，从分割后的图像中获取形状特征、灰度共生矩阵（GLCM）特征和基于Sobel梯度的梯度方向金字塔直方图（SG-PHOG）。一旦本地训练过程完成，它们就会被发送到中央服务器进行全局聚合。最后，全局训练过程有助于有效地检测和分类脑肿瘤。

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