Biomedical Signal Processing and Control最新文献

{"title":"Attention-based hybrid deep learning models and its scientific validation for cardiovascular disease risk stratification","authors":"Mrinalini Bhagawati ,&nbsp;Siddharth Gupta ,&nbsp;Sudip Paul ,&nbsp;Laura Mantella ,&nbsp;Amer M. Johri ,&nbsp;John R. Laird ,&nbsp;Ekta Tiwari ,&nbsp;Narendra N. Khanna ,&nbsp;Andrew Nicolaides ,&nbsp;Rajesh Singh ,&nbsp;Mustafa Al-Maini ,&nbsp;Luca Saba ,&nbsp;Jasjit S. Suri","doi":"10.1016/j.bspc.2025.107824","DOIUrl":"10.1016/j.bspc.2025.107824","url":null,"abstract":"<div><h3>Background</h3><div>Carotid plaque can be used to predict the risk of cardiovascular disease (CVD). Earlier machine learning solutions were not reliable, or accurate. The authors hypothesize that (i) attention-based unidirectional or bidirectional hybrid deep learning (HDL) is superior to non-attention-based unidirectional or bidirectional hybrid deep learning and (ii) attention-based bidirectional hybrid deep learning models are superior to attention-based unidirectional HDL paradigms. The proposed design, AtheroEdge™ 3.0_att-HDL (AtheroPoint™, Roseville, CA, USA), shows how effectively characteristics of the carotid plaque in attention-based hybrid deep learning systems predict the risk of CVD more accurately and reliably.</div></div><div><h3>Methodology</h3><div>The study involved 500 participants who underwent targeted carotid B-mode ultrasonography along with coronary angiography. Six hybrid models (four attention types) were used, totaling 6x4 = 24 models. These were benchmarked against the machine learning models. Mann-Whitney <em>U</em> test, Wilcoxon test, and paired <em>T</em>-test were used for the statistical and reliability tests. The scientific validation was performed using the unseen data. The area-under-the-curve and p-values were used for the performance evaluation of AtheroEdge™ 3.0_att-HDL.</div></div><div><h3>Results</h3><div>The best attention-based bidirectional HDL model showed a mean improvement of <strong>36.11 %</strong>, <strong>5.37 %</strong>, <strong>5.37 %</strong>, and <strong>1.04 %</strong> over Random Forest, unidirectional LSTM, bidirectional LSTM, and best attention-based unidirectional HDL models, respectively. As per the reliability and statistical test findings, the bidirectional AtheroEdge™ 3.0_att-HDL had a p-value of less than 0.001, while the unidirectional AtheroEdge™ 3.0_att-HDL also complied with regulations having a p-value &lt; 0.005.</div></div><div><h3>Conclusions</h3><div>The hypothesis was scientifically validated, assessed for reliability and stability, and deemed suitable for clinical application.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107824"},"PeriodicalIF":4.9,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of insole pressure sensors for ground reaction force estimation through studying principal component analysis","authors":"Amal Kammoun ,&nbsp;Philippe Ravier ,&nbsp;Olivier Buttelli","doi":"10.1016/j.bspc.2025.107848","DOIUrl":"10.1016/j.bspc.2025.107848","url":null,"abstract":"<div><div>In the context of low-cost and portable device for measuring pressure using insole system, selection of the relevant sensors is addressed. In a preliminary step, we compared the accuracy of Ground Reaction Force (GRF) components estimation between two pressure insoles: Fscan and Moticon. This estimation was done using Artificial Neural Network combined with Principal Component Analysis (PCA). Secondly, the focus of this study was to identify the optimal numbers and locations of the pressure sensors by a sensor ranking procedure for both insoles using PCA and three selection strategies. The ranking is determined by analyzing the loss value obtained through PCA for each pressure sensor with three selection strategies. Using data from gold standard force plates, we assessed GRF components estimation accuracies and sensor locations for both insoles during walking activities. As a first result, in our context, Moticon insole yielded superior performance for estimating GRF components compared to Fscan. Secondly, the selection procedure allowed deleting 3 among 16 sensors for Moticon (both feet) and 33/30 (left foot/right foot) among 64 sensors for Fscan. Finally, we have validated these optimal numbers by showing that the quality of GRF components estimation was minimally impacted. Remarkably, both insoles with fewer sensors led to better vertical component estimations. These results should be considered in the context of this study, which does not claim to be generalizable. As these results do not reflect a wide range of activities and subject profiles, it is therefore necessary to re-evaluate these selections with other activity conditions.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107848"},"PeriodicalIF":4.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Slice Segmentation Propagator: Propagating the single slice annotation to 3D volume","authors":"Tianjiao Zhang ,&nbsp;Yanfeng Wang ,&nbsp;Weidi Xie ,&nbsp;Ya Zhang","doi":"10.1016/j.bspc.2025.107874","DOIUrl":"10.1016/j.bspc.2025.107874","url":null,"abstract":"<div><div>In this paper, we consider the problem of semi-automatic medical image segmentation, with the goal of segmenting the target structure in a whole 3-D volume image with only a single slice annotation to relieve the user’s annotation burden. Under such a paradigm, the segmentation of the volume is achieved by establishing the correspondence between slices and propagating the reference segmentation. We propose a more medical-suited framework denoted Slice Segmentation Propagator (SSP) that can establish reliable correspondences between slices with local attention, and maintain a running memory bank that effectively mitigates the problem of error accumulation during mask propagation. Additionally, we propose two test-time training strategies to further improve the propagation performance and generalization ability of the framework, namely, a cycle consistency mechanism to suppress error propagation, and an online adaption procedure via artificial augmentation, assisting the model to better generalize towards new structures at test time. We have conducted thorough experiments on three datasets on four anatomy structures, demonstrating promising results on both in-structure and cross-structure (test on different structures from trainset) scenarios.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107874"},"PeriodicalIF":4.9,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepIH: A deep learning-based near-patient system for treatment recommendation in infantile hemangiomas","authors":"Mengjie Xu ,&nbsp;Zihao Zhao ,&nbsp;Lanzhuju Mei ,&nbsp;Sheng Wang ,&nbsp;Xiaoxi Lin ,&nbsp;Shih-Jen Chang ,&nbsp;Qian Wang ,&nbsp;Yajing Qiu ,&nbsp;Dinggang Shen","doi":"10.1016/j.bspc.2025.107849","DOIUrl":"10.1016/j.bspc.2025.107849","url":null,"abstract":"<div><div>Infantile hemangiomas (IH) are a common pediatric condition that, if not diagnosed and treated early, can lead to functional impairments or permanent disfigurement. However, accurate diagnosis and timely treatment recommendations often depend on the expertise of clinicians and expensive medical imaging, which presents significant challenges in resource-limited settings, especially in low- and middle-income countries. While existing computer-aided diagnosis (CAD) methods have been developed for IH, they mainly assist clinicians rather than offering direct decision-making support, which limits their impact on patient care. To address these challenges, we propose DeepIH, the first near-patient system designed for treatment recommendation of IH based on deep learning. DeepIH is methodologically innovative in two key ways: (1) it accepts camera-shot images as input, enabling patients to conveniently access treatment recommendations through accessible edge devices like smartphones or laptops; (2) it directly generates treatment recommendations, reducing reliance on clinician oversight and enabling faster, more accessible care. Through evaluation on our established dataset, DeepIH achieves an impressive 95.8% accuracy in detecting lesion regions and 84.9% top-3 accuracy in recommending treatments, which even surpasses a fine-tuned foundation model by 1.7%. These findings, for the first time, validate the viability of near-patient diagnosis for IH, highlighting its potential significance in clinical applications as it allows patients to receive treatment recommendations through everyday devices like smartphones or laptops.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"108 ","pages":"Article 107849"},"PeriodicalIF":4.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epileptic seizure prediction method based on transition network data augmentation and fuzzy granular recurrence plot","authors":"Guangyu Yang ,&nbsp;Dafeng Long ,&nbsp;Kai Wang ,&nbsp;Shuyan Xia ,&nbsp;Juncheng Zou","doi":"10.1016/j.bspc.2025.107837","DOIUrl":"10.1016/j.bspc.2025.107837","url":null,"abstract":"<div><div>Prediction of epileptic seizures is crucial for timely intervention and control. A significant challenge in this domain is the scarcity of preictal EEG data, which is important for accurate seizure prediction. To address this issue, a novel data augmentation method based on a transition network is proposed which not only enhances dataset diversity through a random walk algorithm but also preserves the spatial correlation between different EEG channels. Additionally, a noise-robust multivariate weighted fuzzy granular recurrence plot is introduced to extract nonlinear characteristics from EEG data, effectively mitigating the impact of noise on signal analysis. The multivariate weighted fuzzy granular recurrence plots are then input into the Inception V3 model for training the epilepsy prediction model. The novel method achieves state-of-the-art performance on the CHB-MIT database and American Epilepsy Society-Kaggle dataset. The key novelty of this work lies in the proposal of a transition network data augmentation method which overcomes the limitations of existing data augmentation techniques that often ignore inter-channel correlations or distort data distributions. Moreover, the introduction and development of fuzzy granular recurrence plot overcome the noise susceptibility of existing recurrence-plot-based EEG signal analysis methods and improves the extraction of detailed nonlinear features. By integrating these two novel methods into a unified framework, the performances of EEG data analysis and seizure prediction are effectively improved, offering a robust solution for clinical applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107837"},"PeriodicalIF":4.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of abdominal aortic aneurysm using photoplethysmographic signals measured from the index finger","authors":"Mira Haapatikka ,&nbsp;Mikko Peltokangas ,&nbsp;Saara Pietilä ,&nbsp;Sara Protto ,&nbsp;Velipekka Suominen ,&nbsp;Ilkka Uurto ,&nbsp;Damir Vakhitov ,&nbsp;Essi Väisänen ,&nbsp;Karem Lozano Montero ,&nbsp;Mika-Matti Laurila ,&nbsp;Jarmo Verho ,&nbsp;Matti Mäntysalo ,&nbsp;Niku Oksala ,&nbsp;Antti Vehkaoja","doi":"10.1016/j.bspc.2025.107875","DOIUrl":"10.1016/j.bspc.2025.107875","url":null,"abstract":"<div><div>Currently, most of abdominal aortic aneurysms (AAA) are detected by accident on imaging investigations of other medical conditions. The objective of this study was to investigate the classification of subjects with AAA patients and control subjects into two groups using features calculated directly from photoplethysmographic (PPG) signals measured from the index finger. PPG signals were analyzed from 48 test participants from which 25 had AAA and 23 were controls without AAA. Six pulse waveform features were computed from the PPG signals and sequential backward feature selection (SBFS) with linear discriminant analysis (LDA) and leave-one-participant-out cross validation was used to find the most relevant features. The actual classification was also done with LDA using features chosen by the SBFS. The dataset was divided to 70% training and 30% testing groups before classification. The split was stratified so that percentages of AAA subjects and controls was the same in test and train sets. Classification was repeated 500 times, and the median of the classification results was calculated. Three out of six pulse wave features were chosen for the classification. The LDA model had an area under curve (AUC) of 75%, an accuracy of 71%, a specificity of 68%, a sensitivity of 75%, <span><math><msub><mrow><mtext>F</mtext></mrow><mrow><mn>1</mn></mrow></msub></math></span> score of 71%, and a positive predictive value (PPV) of 70%. Features calculated directly from PPG signals can separate individuals with AAA from controls with moderate accuracy. PPG waveform analysis could provide an easy-to-access method for AAA screening. Nonetheless, the performance should still be improved for guaranteeing clinical utility.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107875"},"PeriodicalIF":4.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heel pad’s hyperelastic properties and gait parameters reciprocal modelling by a Gaussian Mixture Model and Extreme Gradient Boosting framework","authors":"Luca Quagliato ,&nbsp;Sewon Kim ,&nbsp;Olamide Robiat Hassan ,&nbsp;Taeyong Lee","doi":"10.1016/j.bspc.2025.107818","DOIUrl":"10.1016/j.bspc.2025.107818","url":null,"abstract":"<div><div>Gait analysis and heel pad mechanical properties have been largely studied by physicians and biomechanical engineers alike. However, only a few contributions deal with the intertwining relationship between these two essential aspects and no research seems to propose a modeling approach to quantitatively correlate them. To bridge this gap, indentation experiments on the heel pad and gait analysis through motion capture camera were carried out on a group composed of 40 male and female subjects in the 20′s to 50′s. To establish a robust correlation between these two sets of parameters, the Gaussian Mixture Model (GMM) features’ enhancement technique was employed and combined with the Extreme Gradient Boosting (XGB) regressor. The hyperelastic constants from models, together with the gait parameters, were employed as both features and target variables in the GMM-XGB architecture showing the ambivalence of the solution and deviations between 5% and 8% in most cases. The results show the strong reciprocal correlation between the individual’s foot plantar soft tissue’s mechanical response and the gait parameters and pave the way for further investigations in the field of biomechanics.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107818"},"PeriodicalIF":4.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TF2AngleNet: Continuous finger joint angle estimation based on multidimensional time–frequency features of sEMG signals","authors":"Hai Jiang ,&nbsp;Yusuke Yamanoi ,&nbsp;Peiji Chen ,&nbsp;Xin Wang ,&nbsp;Shixiong Chen ,&nbsp;Xu Yong ,&nbsp;Guanglin Li ,&nbsp;Hiroshi Yokoi ,&nbsp;Xiaobei Jing","doi":"10.1016/j.bspc.2025.107833","DOIUrl":"10.1016/j.bspc.2025.107833","url":null,"abstract":"<div><div>Current pattern recognition-based myoelectric prosthetic hand control methods map electromyography (EMG) signals to specific hand postures, achieving high accuracy but often resulting in unnatural movements during transitions, reducing the hand’s anthropomorphic nature. While some studies predict single-finger joint angles from EMG signals, these approaches lack practicality since arm muscles often control multiple fingers simultaneously. This study proposed a TF2AngleNet that predicts six finger joint angles using both time domain raw signals and frequency domain features of EMG signals. A novel non-contact joint angle measurement method was used to collect EMG and joint angle data from five healthy subjects over five days. The experimental results demonstrate that TF2AngleNet achieves outstanding performance in continuous joint angle estimation, with a correlation coefficient of 94.7%, an R² value of 89.2%, and an NRMSE of 9.5%. Notably, this represents a 12.43% improvement in NRMSE, along with average gains of 1.2% in CC and 2.42% in R² compared to single-domain models (p-values <span><math><mo>&lt;</mo></math></span> 0.05 across all metrics). Also, hand postures were shown using a virtual hand model, providing a natural and bionic control method of myoelectric hands. Additionally, a novel conceptual framework is proposed to reduce barriers to using pattern recognition-based prosthetic hands, with this study serving as its first stage by validating the model’s performance under three experimental conditions. This research provides a promising solution for dexterous, biomimetic and practical myoelectric prosthetic hand control methods.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107833"},"PeriodicalIF":4.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Holistic evaluation and generalization enhancement of CART-ANOVA based transfer learning approach for brain tumor classifications","authors":"Shiraz Afzal,&nbsp;Muhammad Rauf","doi":"10.1016/j.bspc.2025.107829","DOIUrl":"10.1016/j.bspc.2025.107829","url":null,"abstract":"<div><div>This study presents a convolutional neural network (CNN) based on enhanced detection of brain tumors. The promising detection is made possible by the CART-ANOVA technique by applying preprocessing methods to improve testing dataset quality. While utilizing two sources of the dataset for both inter and intra-dataset validation, image sharpening techniques are utilized to refine the Source 2 and Source 1 testing datasets, leading to improved model performance and robustness in brain tumor classification. This paper introduces a hyper-parameter tuning model, designed to determine optimal parameters focusing on batch size and learning rate for the authentic classification. By providing statistical validation, this model ensures the selection of the most effective hyperparameters, leading to superior classification performance. The ResNet18 model was initially trained on one dataset, having 20 % of the data reserved for testing. To further evaluate its robustness and generalizability, the model was tested on a second dataset. The framework produces astonishing results, attaining 99.65 % for four tumor classifications and 98.05 % for seven tumor categories on the dataset from Source 1. The introduced data preprocessing methods resulted in 99.31 % accuracy for four distinct tumor classifications and 98.90 % for seven distinct tumor classifications on Source 2, while also improving Source 1 accuracy to 99.84 % (four-class) and 99.03 % (seven-class). By achieving seven distinct classifications, this work not only improves accuracy and variability but also strengthens model robustness through a rigorous post-validation framework. These advancements offer significant potential for improving brain tumor diagnosis and treatment strategies.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107829"},"PeriodicalIF":4.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging motor execution and motor imagery BCI paradigms: An inter-task transfer learning approach","authors":"Sergio Pérez-Velasco,&nbsp;Diego Marcos-Martínez,&nbsp;Eduardo Santamaría-Vázquez,&nbsp;Víctor Martínez-Cagigal,&nbsp;Roberto Hornero","doi":"10.1016/j.bspc.2025.107834","DOIUrl":"10.1016/j.bspc.2025.107834","url":null,"abstract":"<div><div>Motor imagery (MI)-based brain–computer interfaces (BCIs) decode movement imagination from brain activity, but improving decoding accuracy from electroencephalography (EEG) remains challenging. MI-based BCIs require calibration runs to train models; however, participant engagement cannot be externally verified. Motor execution (ME) is more straightforward and can be supervised. Deep learning (DL) leverages transfer learning (TL) to bypass calibration. This is the first work to explore wether a ME-trained DL model can reliably classify MI without finetuning to the MI task, thereby achieving direct TL between ME and MI tasks. We employed EEGSym, a DL network for inter-subject TL of EEG decoding, evaluating three scenarios: ME to MI, ME to ME, and MI to MI classification. We analyzed performance correlation between scenarios, and used shapley additive explanations (SHAP) to elucidate model focus patterns learned from ME or MI data. Results show that DL models trained on ME data and tested on MI perform comparably to those trained on MI data. A significant positive correlation was found between performance in ME and MI tasks for models trained on ME data. Explainable artificial intelligence (XAI) techniques revealed robust correlation between patterns in ME and MI tasks. However, between 0.5 to 1 s, the ME-trained model focused on the contralateral central region, while the MI-trained model also targeted the ipsilateral fronto-central region. Our findings demonstrate the viability of inter-task TL between ME and MI using DL models in BCI applications. This supports using ME-trained models for MI tasks to enhance targeted learning of brain activation patterns.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107834"},"PeriodicalIF":4.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}