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学者姓名：李玉榕
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目的 基于OpenSim软件开发建立1个包含双接触点、3个自由度膝关节的个性化肌肉骨骼模型,估测膝关节负荷,为临床膝骨关节炎(KOA)患者的精准化诊断及个体化康复方案的制订提供可靠依据.方法 利用由斯坦福大学授权公开、加州拉霍亚市Scripps诊所希利矫形研究与教育中心发布的"膝关节体内负荷预测挑战赛"公开数据集,纳入4例接受全膝关节置换术(TKA)并植入压力检测仪器的受试者,以OpenSim模型库中的通用模型gait2392为基础升级搭建,新模型包含28个关节和43个自由度,并调整膝关节结构,使其具有屈曲-伸展、内收-外展、内旋-外旋3个方向自由度,增添了单臂4个关节、7个自由度能够扭矩驱动的上肢结构,并将股骨与胫骨间由单接触点调整为双接触点.通过模型缩放、逆向运动学分析、逆向动力学分析、残差缩减处理、肌肉控制和肌肉分析等,综合考虑步态中外部力和体内肌力的贡献,计算胫股关节内侧、外侧接触力.采用Matlab 2018b进行统计学分析,计算模型估计结果与植入假体测量结果的Pearson相关系数、均方根误差以及标准差,验证本研究建立的肌骨模型的有效性.结果 4名受试者共获得21次完整步态试验数据,受试者JW、DM、PS、SC步态周期平均时长分别为(1.18±0.03)、(1.18±0.08)、(1.09±0.02)、(1.14±0.04)s;内侧、外侧、总接触力估计值与测量值的相关系数平均值分别为(0.921±0.079)、(0.817±0.084)、(0.930±0.066),均方根误差的平均值分别为(0.336±0.146)、(0.332±0.043)、(0.442±0.160)BW;内侧、外侧接触力峰值的估计值与测量值均方根误差的平均值分别为(0.43±0.25)、(0.34±0.24)BW,峰值出现时间误差的平均值分别为(44.09±34.66)、(67.52±61.19)ms.结论 利用所建立的双接触点、3个自由度的膝关节全身OpenSim肌肉骨骼模型能够生成肌肉驱动的步态模拟,得到可靠的胫股关节接触力,为精准监测胫股关节负荷、临床康复方案的制订和持续改进假体设计等提供重要依据.
Keyword ：
OpenSim OpenSim 内侧、外侧接触力 内侧、外侧接触力 肌肉骨骼模型 肌肉骨骼模型 胫股关节 胫股关节 行走步态 行走步态
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| GB/T 7714 | 王晓玲 , 简家伟 , 谢秋蓉 et al. 基于OpenSim肌骨建模估算胫股关节接触力 [J]. | 康复学报 , 2025 , 35 (1) : 59-68 . |
| MLA | 王晓玲 et al. "基于OpenSim肌骨建模估算胫股关节接触力" . | 康复学报 35 . 1 (2025) : 59-68 . |
| APA | 王晓玲 , 简家伟 , 谢秋蓉 , 连章汇 , 郭春明 , 郭洁梅 et al. 基于OpenSim肌骨建模估算胫股关节接触力 . | 康复学报 , 2025 , 35 (1) , 59-68 . |
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Background and Objectives: Electrophysiological source imaging (ESI) is a challenging technique for noninvasively measuring brain activity, which involves solving a highly ill-posed inverse problem. Traditional methods attempt to address this challenge by imposing various priors, but considering the complexity and dynamic nature of the brain activity, these priors may not accurately reflect the true attributes of brain sources. In this study, we propose a novel deep learning-based framework, spatiotemporal source imaging network (SSINet), designed to provide accurate spatiotemporal estimates of brain activity using electroencephalography (EEG). Methods: SSINet integrates a residual network (ResBlock) for spatial feature extraction and a bidirectional LSTM for capturing temporal dynamics, fused through a Transformer module to capture global dependencies. A channel attention mechanism is employed to prioritize active brain regions, improving both the accuracy of the model and its interpretability. Additionally, a weighted loss function is introduced to address the spatial sparsity of the brain activity. Results: We evaluated the performance of SSINet through numerical simulations and found that it outperformed several state-of-the-art ESI methods across various conditions, such as varying numbers of sources, source range, and signal-to-noise ratio levels. Furthermore, SSINet demonstrated robust performance even with electrode position offsets and changes in conductivity. We also validated the model on three real EEG datasets: visual, auditory, and somatosensory stimuli. The results show that the source activity reconstructed by SSINet aligns closely with the established physiological basis of brain function. Conclusions: SSINet provides accurate and stable source imaging results.
Keyword ：
Deep learning Deep learning Electroencephalography (EEG) Electroencephalography (EEG) Electrophysiological source imaging (ESI) Electrophysiological source imaging (ESI) Ill-posed inverse problem Ill-posed inverse problem
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| GB/T 7714 | Shi, Wuxiang , Li, Yurong , Zheng, Nan et al. A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2025 , 266 . |
| MLA | Shi, Wuxiang et al. "A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 266 (2025) . |
| APA | Shi, Wuxiang , Li, Yurong , Zheng, Nan , Hong, Wenyao , Zhao, Zhenhua , Chen, Wensheng et al. A deep learning framework leveraging spatiotemporal feature fusion for electrophysiological source imaging . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2025 , 266 . |
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In this paper, an innovative Sparse Bayesian Learning (SBL)-based modeling approach incorporating Information Entropy (IE) to enhance the stability is developed to create Nonlinear Auto-Regressive model with eXogenous input, aiming to address the challenges of low estimation accuracy, limited computational efficiency and insufficient sparsity in existing methods. This development is conducive to capture the key features of cortical responses when focusing on neural activity, providing more accurate results for studying brain mechanisms. By introducing identity transformations and optimizing parameter update and stopping strategies, both computational efficiency and estimation accuracy of the SBL algorithm are effectively improved, where the iterative matrix within ISBL is refined by the introduced IE, which further strengthens the algorithm performance at low Signal-to-Noise Ratio levels. Extensive evaluation demonstrates the proposed method reduces the error by 48 %, decreases the traditional SBL method's runtime by 70%, and achieves the sparsest result while maintaining structural accuracy, which shows significant competitiveness in accuracy, efficiency and sparsity as compared to other state-of-the-art methods. Moreover, the analysis of real EEG signals indicates that the brain's response follows a fundamental rhythm pattern of adaptation to both active and passive tasks, and such adaptive process can be effectively captured by the proposed sparse model through the combination of linear and nonlinear terms, each serving distinct roles. These findings offer a novel insight into the human sensorimotor system, which indicates the great potential of the proposed method in assessing sensorimotor impairments and exploring effective clinical intervention method.
Keyword ：
EEG EEG Information entropy Information entropy NARX NARX Sensory response mechanisms Sensory response mechanisms Sparse Bayesian Learning Sparse Bayesian Learning
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| GB/T 7714 | Zheng, Nan , Li, Yurong , Shi, Wuxiang et al. Sparse Bayesian based NARX modeling of cortical response: Introducing information entropy for enhancing the stability [J]. | NEUROCOMPUTING , 2025 , 626 . |
| MLA | Zheng, Nan et al. "Sparse Bayesian based NARX modeling of cortical response: Introducing information entropy for enhancing the stability" . | NEUROCOMPUTING 626 (2025) . |
| APA | Zheng, Nan , Li, Yurong , Shi, Wuxiang , Xie, Qiurong . Sparse Bayesian based NARX modeling of cortical response: Introducing information entropy for enhancing the stability . | NEUROCOMPUTING , 2025 , 626 . |
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Background: Electrocardiogram (ECG) is crucial in diagnosing and preventing heart diseases. However, its efficacy is compromised by the interference of the external environment, leading to potential misdiagnoses. Thus, it is crucial to remove the noise in ECGs. Recently, deep-learning based ECGs denoising approaches have achieved impressive performance, however, they only considered the time-domain information of ECGs. Methods: In this work, we propose a Frequency Information Enhanced Half Instance Normalization Network (FIEHINet) which integrates knowledge of both time and frequency domains into a deep-learning model for ECG signal denoising. Two branches are employed to extract time and frequency features for noise eliminating, respectively. Then the ECG signals are reconstructed based on the fused features. Furthermore, masked signal training is introduced to improve the generalization ability. Results: In order to evaluate the proposed method, ECGs used are chosen from five different databases. The proposed method for ECG signal denoising achieved Sum of Squared Distances scores of 3.95 +/- 7.04, 2.04 +/- 3.20, and 0.998 +/- 1.579 for three kinds of noise intensities. Meanwhile, the classification experimental results of the processed dataset with the proposed method are 3.8 % higher in F1 score than the original dataset. Conclusion: A model for removing mixed noises is successfully developed and tested. Significance: This study presents an ECG denoising technique based on Half Instance Normalization, time- -frequency information, and masked signal training, which can improve ECG interpretation and potentially reduce misdiagnoses in clinical practice.
Keyword ：
Denoising Denoising Electrocardiograms Electrocardiograms Frequency Information Enhanced (FIE) Frequency Information Enhanced (FIE) Half Instance Normalization (HIN) Half Instance Normalization (HIN) Neural Network Neural Network
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| GB/T 7714 | Gao, Ning , Li, Yurong , Zheng, Nan et al. Frequency information enhanced half instance normalization network for denoising electrocardiograms [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 102 . |
| MLA | Gao, Ning et al. "Frequency information enhanced half instance normalization network for denoising electrocardiograms" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 102 (2025) . |
| APA | Gao, Ning , Li, Yurong , Zheng, Nan , Shi, Wuxiang , Cai, Dan , Huang, Xiaoying et al. Frequency information enhanced half instance normalization network for denoising electrocardiograms . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 102 . |
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Traditional muscle strength training instruments often rely on torque-based feedback to guide exercises, which can introduce delays in system response and result in discomfort due to hysteresis effects. Surface electromyography (sEMG) signals were used as control inputs to overcome the lag in torque-based muscle strength training instruments. The sEMG is generated 20-80 ms before movement, which is called "muscle electromechanical delay." If the torque can be effectively predicted during this period, the lag effect can be significantly reduced, thus improving the effectiveness and comfort of training. We, therefore, proposed a multistep ahead (MSA) model based on the nonlinear autoregressive network with exogenous inputs (NARX) dynamic recurrent neural network. It predicted torques using sEMG, and allowed natural control of the instrument. The results showed that the normalized root-mean-square error (NRMSE) was lower than 0.1167, and the Pearson correlation coefficients (rho) exceeded 0.9444, even when the ahead steps achieved 35. The intrasubject and the intersubject validation demonstrated significantly lower NRMSE ( p <0.05) and higher rho ( p < 0.05) of the MSA model, compared with some state-of-theart recursive models and typical models without autoregression items. It proves that the MSA can accurately predict the motion. Meanwhile, the introduction of the sEMG signal as a control source significantly reduced the root-mean-square jerk (RMSJ) of the torque, demonstrating smoother motion. The experimental results revealed that the one-step-ahead model achieved an average response time of 3.73 ms, which is markedly lower than the muscle electromechanical delay. The response time increased by an average of approximately 0.068 ms per additional ahead step. In conclusion, the proposed EMG-driven muscle strength training instrument enables natural muscle strength training.
Keyword ：
Brain modeling Brain modeling Computational modeling Computational modeling Electromyography Electromyography Instruments Instruments Limbs Limbs Muscles Muscles Muscle strength training instrument Muscle strength training instrument neural network neural network nonlinear autoregressive network with exogenous inputs (NARX) nonlinear autoregressive network with exogenous inputs (NARX) Predictive models Predictive models Sensors Sensors surface electromyography (sEMG) surface electromyography (sEMG) Torque Torque torque prediction torque prediction Training Training
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| GB/T 7714 | Li, Yurong , Lin, Xiaofeng , Liu, Qiang et al. Natural Control of Muscle Strength Training Instrument Based on EMG-Driven Multistep Ahead Models [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Li, Yurong et al. "Natural Control of Muscle Strength Training Instrument Based on EMG-Driven Multistep Ahead Models" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Li, Yurong , Lin, Xiaofeng , Liu, Qiang , Zheng, Nan , Tan, Jiyu , Zhan, Miaoqin . Natural Control of Muscle Strength Training Instrument Based on EMG-Driven Multistep Ahead Models . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
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Atrial fibrillation (AF) is a common arrhythmia often associated with cardiovascular diseases such as stroke and heart failure. Although numerous researchers have made substantial progress in AF detection using deep learning methods in recent years, most of these methods require extensive computational resources. Moreover, the clinical application of these models is challenging due to the black-box nature of deep learning models. Therefore, this paper proposes a lightweight AF detection model based on feature fusion and conducts an interpretability study. The model comprises an ECG (ElectroCardioGram) backbone network and an RRI (R-R Interval) branch. The ECG backbone network uses depthwise separable convolutions along with a few standard convolutions to extract deep morphological features of the ECG signals, while the RRI branch employs multi-scale convolutions to extract deep rhythm features of the RRI. The network learns robust feature representations by fusing morphological features and rhythm features to detect AF accurately. As to interpretability analysis, Grad-CAM++ is utilized to visualize the contribution of different features to the classification results. In this paper, the training and dataset internal tests are conducted in the LTAFDB and achieved an accuracy of 97.99%. In order to validate the generalization performance of the model, external testing experiments are conducted using the AFDB and the CPSC2021, achieving an accuracy of 95.17% and 93.81%, respectively. Experimental results demonstrate that the proposed method is lightweight, stable, and accurate, and the incorporation of interpretable deep-learning techniques suggests that the proposed method holds significant potential for the clinical diagnosis of AF. © 2025 Chinese Institute of Electronics. All rights reserved.
Keyword ：
Deep learning Deep learning Electrocardiography Electrocardiography Visualization Visualization
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| GB/T 7714 | Gao, Ning , Li, Yu-Rong , Chen, Hong et al. Lightweight Atrial Fibrillation Model Based on Feature Fusion of Morphology and Rhythmic and Interpretability Analysis [J]. | Acta Electronica Sinica , 2025 , 53 (2) : 503-513 . |
| MLA | Gao, Ning et al. "Lightweight Atrial Fibrillation Model Based on Feature Fusion of Morphology and Rhythmic and Interpretability Analysis" . | Acta Electronica Sinica 53 . 2 (2025) : 503-513 . |
| APA | Gao, Ning , Li, Yu-Rong , Chen, Hong , Chen, Wen-Sheng , Jia, Zi-Hao . Lightweight Atrial Fibrillation Model Based on Feature Fusion of Morphology and Rhythmic and Interpretability Analysis . | Acta Electronica Sinica , 2025 , 53 (2) , 503-513 . |
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为解决传统基于脑电信号（ElectroencEphaloGraphy，EEG）的单模态脑机接口（Brain-Computer Interface，BCI）技术存在的空间分辨率低、易受噪声干扰等问题，越来越多的研究开始关注基于 EEG 信号和功能近红外光谱 （functional Near-InfRared Spectroscopy，fNIRS）信号融合的BCI研究. 然而，这两种异构信号之间的融合具有挑战性，本文创新性地提出一种基于深度学习和证据理论的端对端信号融合方法，用于运动想象（Motor Imagery，MI）分类. 对于EEG信号，本文通过双尺度时间卷积和深度可分离卷积提取其时空特征信息，并引入混合注意力模块以增强网络对重要特征的感知能力. 对于fNIRS信号，本文通过全通道的空间卷积探索大脑不同区域之间的激活差异，并通过并联时间卷积和门控循环单元（Gated Recurrent Unit，GRU）模块捕获更丰富的时间特征信息 . 在决策融合阶段，首先将两种信号分别解码得到的决策输出利用 Dirichlet 分布参数估计，以量化不确定性；然后使用 Dempster-Shafer 理论（Demp⁃ster-Shafer Theory，DST）进行双层推理，从而融合来自两种基本信念分配（Basic Belief Assignment，BBA）方法和不同模态的证据，得到最终的分类结果. 本文基于公开数据集TU-Berlin-A进行模型的测试评估，获得了83.26%的平均准确率，相较于最先进研究提升了3.78%，该结果为基于EEG和fNIRS信号的融合研究提供了新的思路和方法.
Keyword ：
Dempster-Shafer理论 Dempster-Shafer理论 功能近红外光谱（fNIRS）信号 功能近红外光谱（fNIRS）信号 深度学习 深度学习 混合脑机接口（BCI） 混合脑机接口（BCI） 脑 电信号（EEG）信号 脑 电信号（EEG）信号 运动想象（MI） 运动想象（MI）
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| GB/T 7714 | 康冉斓 , 李玉榕 , 史武翔 et al. 基于Dempster-Shafer证据推理的EEG-fNIRS 运动想象分类决策层融合方法 [J]. | 电子学报 , 2025 . |
| MLA | 康冉斓 et al. "基于Dempster-Shafer证据推理的EEG-fNIRS 运动想象分类决策层融合方法" . | 电子学报 (2025) . |
| APA | 康冉斓 , 李玉榕 , 史武翔 , 李吉祥 . 基于Dempster-Shafer证据推理的EEG-fNIRS 运动想象分类决策层融合方法 . | 电子学报 , 2025 . |
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金线莲是一种珍贵中药材,其治疗、保健作用十分显著.金线莲培育方式主要有种植、组培等,不同培育方式的金线莲,在性状上仅表现出细微差异,但药用、市场价值差异显著,培育方式鉴别能有效保证药用疗效、维护良好市场秩序,然而由于不同品系、产地、培育时间等复合差异的影响,增加了培育方式鉴别难度与复杂度.提出一种基于改进1D-Inception-CNN模型的金线莲培育方式鉴别方法.采用近红外光谱仪采集种植、组培金线莲的光谱,首先使用合成少数类过采样技术(SMOTE)进行过采样以解决种植品、组培品样本比例不平衡问题,其次构建基于改进Inception结构的一维卷积神经网络对来自不同品系、产地、培育时间的金线莲进行种植品、组培品鉴别,最后采用贝叶斯优化方法对构建的卷积神经网络模型超参数进行优化;最终五折交叉验证平均鉴别准确率、精确率、召回率、综合评价指标高达97.95％、96.16％、100％、98.02％.研究表明,实验提出的鉴别模型为快速鉴别金线莲种植品、组培品提供一种有效方法.
Keyword ：
Inception模块 Inception模块 一维卷积神经网络 一维卷积神经网络 少数类过采样技术 少数类过采样技术 贝叶斯优化 贝叶斯优化 金线莲 金线莲
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| GB/T 7714 | 蓝艳 , 王武 , 许文 et al. 基于SMOTE和Inception-CNN的种植和组培金线莲鉴别 [J]. | 光谱学与光谱分析 , 2024 , 44 (1) : 158-163 . |
| MLA | 蓝艳 et al. "基于SMOTE和Inception-CNN的种植和组培金线莲鉴别" . | 光谱学与光谱分析 44 . 1 (2024) : 158-163 . |
| APA | 蓝艳 , 王武 , 许文 , 柴琴琴 , 李玉榕 , 张勋 . 基于SMOTE和Inception-CNN的种植和组培金线莲鉴别 . | 光谱学与光谱分析 , 2024 , 44 (1) , 158-163 . |
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Precise time-frequency (TF) analysis of electroencephalogram (EEG) signals is critical in evaluating cortical responses of patients with cervical dystonia (CD). Traditional methods are faced with challenges of constrained time-frequency resolution and accuracy, limiting the application of EEG in CD patients. This study introduces a novel adaptive basis function-based for TF representation method to meet the challenge. The methodology begins by identifying the kernel function center through an adaptive clustering technique. Then, the optimum structures and scales of the kernel function are determined by the improved genetic algorithm, which enable more precise tracking of EEG signals. Finally, accurately estimated parameters are converted to high-resolution TF images using a parameter spectrum estimation method, providing more detailed information of the EEG data. Leveraging the insights from the TF images, a regression model correlating TF features with clinical scores was developed to assess severity of CD patients. Simulation results show that the proposed method has superior tracking capabilities and a higher time-frequency resolution than current state-of-the-art methods. In the analysis of real EEG signals, we observed a notable elevation in gamma band power within the C3 and P3 channels, significantly differing from healthy individuals (p < 0.05), however, which cannot be found by other methods. This indicates distinctive high-frequency cortical activation associated with CD. Moreover, the regression model reaches a correlation coefficient above 0.82, suggesting its potential for objectively assessing severity of CD patients. Collectively, this study provides a robust tool for EEG signal analysis, and the analysis result will contribute to clinic treatment.
Keyword ：
Adaptive Radial Basis Functions Adaptive Radial Basis Functions EEG signals EEG signals Improved genetic algorithm Improved genetic algorithm Regression analysis Regression analysis Time-frequency analysis Time-frequency analysis Time-varying parameter estimation Time-varying parameter estimation
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| GB/T 7714 | Zheng, Nan , Li, Yurong . Adaptive radial basis functions based Time-Varying model for EEG analysis in patients with cervical dystonia [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
| MLA | Zheng, Nan et al. "Adaptive radial basis functions based Time-Varying model for EEG analysis in patients with cervical dystonia" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 92 (2024) . |
| APA | Zheng, Nan , Li, Yurong . Adaptive radial basis functions based Time-Varying model for EEG analysis in patients with cervical dystonia . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2024 , 92 . |
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Accurate detection of muscle activity is crucial for rehabilitation systems that rely on voluntary control. However, the presence of false background spikes interference can significantly impede the precise decoding of motion intention. To address this problem, an adaptive two-step method is proposed in this paper for accurately extracting the muscle activation intervals from sEMG signals. In the first step, an adaptive threshold is used to identify potential onsets and offsets of the envelope. Then, a combination of an evaluation equation and the k-means clustering technique is utilized to eliminate incorrect onsets and offsets. In the second step, two peak points closing proximity to the onset and offset are identified. The tangency of the envelope's onset and offset with the respective peak points is then determined, with the intersection point of these tangencies is considered as the final onset and offset. The proposed method is tested on semi-synthetic sEMG signals and real sEMG signals, and compared with state-of-the-art algorithms. The results clearly indicate that the proposed method produces the best detection performance, and eliminates the requirement for parameter selection, greatly facilitating the signal extraction process.
Keyword ：
adaptive two-step method adaptive two-step method muscle activity detection muscle activity detection signal processing signal processing surface electromyography signal surface electromyography signal
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| GB/T 7714 | Zheng Nan , Li Yurong , Zhan Miaoqin . An Adaptive Two-Step Method for Voluntary Muscle Activity Detection Using sEMG Signals with False Background Spikes [J]. | 2024 10TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, CONTROL AND ROBOTICS, EECR 2024 , 2024 : 274-278 . |
| MLA | Zheng Nan et al. "An Adaptive Two-Step Method for Voluntary Muscle Activity Detection Using sEMG Signals with False Background Spikes" . | 2024 10TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, CONTROL AND ROBOTICS, EECR 2024 (2024) : 274-278 . |
| APA | Zheng Nan , Li Yurong , Zhan Miaoqin . An Adaptive Two-Step Method for Voluntary Muscle Activity Detection Using sEMG Signals with False Background Spikes . | 2024 10TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, CONTROL AND ROBOTICS, EECR 2024 , 2024 , 274-278 . |
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