Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献

{"title":"Signal extension with SeU-net for boosting the decoding performance of short-time SSVEP-based brain-computer interfaces.","authors":"Hui Li, Guanghua Xu, Shiyu Zhang, Jieren Xie, Chengcheng Han, Qingqiang Wu, Sicong Zhang","doi":"10.1109/EMBC58623.2025.11253264","DOIUrl":"10.1109/EMBC58623.2025.11253264","url":null,"abstract":"<p><p>Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (SSVEP-BCIs) have greatly benefited the lives of patients. However, existing SSVEP recognition methods exhibit poor performance on short SSVEP signals. SSVEP recognition accuracy heavily depends on signal length, which increases as the signal length. From a novel data perspective, this study proposes a signal extension method called SeU-net without requiring calibration data from the target subject to improve the recognition performance of calibration-free methods for short-time SSVEP signals. SeU-net employs LSTM and contrastive learning to enhance feature extraction, converting signals from sample space to feature space, and then back to the sample space to realize signal extension. SeU-net is designed to focus only on signal extension in the temporal domain, without subject-specific feature extraction operations, resulting in strong cross-subject signal extension performance. The extensive experiments demonstrate that SeU-net significantly enhances the decoding performance of calibration-free methods for short-time SSVEP signals. By enabling more accurate decoding with shorter SSVEP signals, SeU-net holds the potential to advance the practical application of high-speed SSVEP-BCIs further.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"U-Grad: A Grad-CAM-Guided Reduced U-Net for Efficient Lung Cancer Segmentation.","authors":"Giulia Bruschi, Agnese Sbrollini, Mattia Carletti, Mhd Jafar Mortada, Laura Burattini","doi":"10.1109/EMBC58623.2025.11253530","DOIUrl":"10.1109/EMBC58623.2025.11253530","url":null,"abstract":"<p><p>Lung cancer is the most common cause of cancer-related death worldwide. The detection of lung nodules from Computed Tomography (CT) scans is essential for assessing disease progression, monitoring treatment response, and guiding therapeutic strategies. Deep learning has emerged as a powerful tool for image segmentation, demonstrating significant potential in medical imaging applications. This work aims to introduce U-Grad, a novel model designed for lung nodule segmentation from 2D CT slices. It integrates an encoder that generates heatmaps using Gradient-weighted Class Activation Mapping (Grad-CAM), which are then concatenated with CT slices and fed into a Reduced U-Net to enhance nodule representation. The Reduced U-Net is characterized by an encoder-decoder structure whose maximum depth, in terms of filter size, is (256,256), Additionally, it employs the Leaky Rectified Linear Unit as an alternative activation function, enhancing its representational capacity. NSCLC Radiogenomics dataset from The Cancer Imaging Archive was used to train and test the proposed U-Grad for 100 epochs. The performance of both the Reduced U-Net and U-Grad models was evaluated using the Dice Coefficient (DC) and the Intersection over Union (IoU) metrics. The results demonstrate that both models outperform existing models in the literature. The Reduced U-Net achieves a DC of 93.15% and an IoU of 89.02%, while U-Grad achieves a DC of 91.27% and an IoU of 86.26% in test set. Although both models exhibit comparable performance, U-Grad demonstrates slightly lower overfitting, making it a more robust alternative. Moreover, U-Grad's ability to generate interpretable heatmaps enhances its utility for clinical applications and research, particularly in resource-limited settings where annotated data are scarce.Clinical relevance- U-Grad is an innovative and effective lung nodule segmentation model that leverages explainable AI techniques to enhance its performance, interpretability and generalizability.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do Larger Resections Cut It? Relating Temporal Lobe Epilepsy Surgery and Seizure Outcome.","authors":"Callum M Simpson, Jonathan Horsley, Vytene Janiukstyte, Jane de Tisi, Anna Miserocchi, Andrew McEvoy, Yujiang Wang, John S Duncan, Peter N Taylor","doi":"10.1109/EMBC58623.2025.11253381","DOIUrl":"10.1109/EMBC58623.2025.11253381","url":null,"abstract":"<p><p>Anterior temporal lobe resection (ATLR) results in seizure freedom in half of individuals with drug-resistant temporal lobe epilepsy (TLE). Some investigators have suggested that larger resections lead to greater chance of seizure freedom, while others report no relationship. In this study, we examine the relationship between resection size and seizure freedom through (i) total volume analysis and (ii) a mass univariate regional approach.Patient demographics and resection volumes were collected for 283 patients who underwent subsequent ATLR, and seizure freedom was measured after 12 months. Additionally, the percentage resection of each Desikan-Kiliany parcellated region was calculated. We computed the AUC to measure effect sizes and used Wilcoxon ranksum tests to assess significance.Total resection volumes were larger in males than females, and larger in right than left ATLR. However, when scaled to percentage of brain tissue resected, only the hemisphere difference remained. There was no significant association of total or regional resection volume with post-operative seizure freedom.Larger resections in males are due to their larger total brain volumes. Smaller left-sided resections reflect the more conservative surgical approach in the language dominant hemisphere. Within the normal ranges of a typical ATLR, larger resection volumes do not increase chance of seizure-freedom. Future studies should investigate the details of the resection of gray matter, such as piriform cortex, and white matter tracts that can form epileptogenic networks.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotion Recognition with Portable EEG in Immersive 360-Degree Environment.","authors":"Junkai Huang, Weixuan Huang, Tsz Ching Rachel Lin, Pengpai Wang, Chuanliang Han, Chim Sum Wong, Paul Heinrich Bethge, Jeffrey Shaw, Rosa H M Chan","doi":"10.1109/EMBC58623.2025.11253461","DOIUrl":"10.1109/EMBC58623.2025.11253461","url":null,"abstract":"<p><p>This study aimed to explore the feasibility of using portable single-channel dry electrode electroencephalography (EEG) headbands to identify and distinguish human emotions elicited by multimodal stimuli presented in a 360-degree immersive environment. Such an environment was specifically chosen to facilitate naturalistic perception, in contrast to the conventional presentation of stimuli through a flat screen and headphones in the laboratory setting. To this end, this study designed multimodal stimulation and recorded the subjective scores of the subjects in multiple emotional dimensions through a self-rating scale. The differential entropy (DE) feature was used to capture the dynamic changes and complexity of the EEG signal. A variety of classic machine learning (ML) models were used for classification, and the feature performance and model effectiveness were compared and analyzed. The results show that after removing most artifacts and applying DE features, single-channel EEG signals can effectively distinguish different emotional states measured under multimodal stimulation. In summary, this study provides empirical support for emotion recognition using single-channel EEG in a 360-degree immersive environment, which allowed for naturalistic perception while maintaining the advantages of a controlled setting. This marks a step toward multi-user applications by leveraging the portability and convenience of portable devices.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning Method for Autism Spectrum Disorder Classification Based on Multimodal Neuroimaging Data<sup />.","authors":"Xiaowen Liu, Bing Niu, Tiancheng Cao, Fuxue Chen","doi":"10.1109/EMBC58623.2025.11253912","DOIUrl":"10.1109/EMBC58623.2025.11253912","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication skills. Accurate, early-stage differentiation of individuals with ASD from typically developing controls (TC) is essential for timely intervention and treatment. In this paper, we propose a predictive model based on multimodal feature fusion, using both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) data to improve the classification of ASD. By integrating complementary information from these two modalities, our method constructs a more comprehensive feature space, capturing complex neuropathological signatures that a single modality cannot provide. We evaluated the proposed approach using imaging data from the ABIDE NYU site under a five-fold cross-validation scheme. The experimental results show that the proposed method achieved an average accuracy of 82.63%, an area under the receiver operating characteristic curve (AUC) of 89.31%, a sensitivity of 81.45%, and a specificity of 82.86%. These findings suggest that the proposed multimodal feature fusion strategy significantly enhances ASD identification, offering a promising approach to the precise diagnosis of brain disorders.Clinical Relevance- We proposed a learning framework that integrates multi-modality neuroimaging data, addressing the heterogeneity of ASD-related brain features and the challenges posed by limited training data. This framework contributes to improving diagnostic accuracy and supports early clinical decision-making for ASD, thereby facilitating timely intervention and the development of personalized treatment strategies in clinical practice.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145670320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of Speckle Contrast Optical Spectroscopy for Quantifying Active Muscle Blood Flow.","authors":"Tomoya Yamamoto, Vika Tarle, Masashi Ichinose, Nisan Ozana, Yumie Ono","doi":"10.1109/EMBC58623.2025.11254183","DOIUrl":"10.1109/EMBC58623.2025.11254183","url":null,"abstract":"<p><p>The quantification of muscle blood flow is crucial in fields such as exercise physiology and rehabilitation medicine. While diffuse correlation spectroscopy (DCS) is a well-established, noninvasive method for measuring tissue blood flow, its widespread use is hindered by the need for expensive photon counters and its relatively low signal-to-noise ratio (SNR). Speckle contrast optical spectroscopy (SCOS), a novel technique utilizing a megapixel camera (e.g., a complementary metal- oxide-semiconductor camera), has emerged as a promising alternative. This method utilizes spatial speckle contrast to measure blood flow, offering a higher SNR at a reduced cost. This study evaluates the feasibility of SCOS in quantifying muscle blood flow under active conditions and compares its performance directly with that of DCS. The results indicate that SCOS is consistent with DCS in terms of relative blood flow index measurements, and its SNR surpasses that of DCS, particularly in detecting pulsatile blood flow. These findings position SCOS as a cost-effective, high-precision tool for monitoring skeletal muscle microcirculation during physical activities, potentially enhancing clinical and research applications in vascular function assessment.Clinical Relevance- SCOS presents a cost-effective alternative to DCS for the assessment of blood flow in active muscles, offering a superior SNR and enhanced detection of pulsatile flow. This advantage makes it valuable for evaluating vascular function in exercise physiology and rehabilitation contexts.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Modeling of Ultradian Rhythms Using the Hankel Alternative View of Koopman (HAVOK) Analysis.","authors":"Emmanuel Molefi, Billy C Smith, Christopher Thornton, Peter N Taylor, Yujiang Wang","doi":"10.1109/EMBC58623.2025.11254865","DOIUrl":"10.1109/EMBC58623.2025.11254865","url":null,"abstract":"<p><p>Ultradian rhythms - quasi-rhythmic fluctuations in behavior and physiology with periods shorter than 24 hours - are observed across various organisms, including humans. Despite their role in key biological processes such as sleep architecture and hormone regulation, their underlying mechanisms remain poorly understood. Here, we leveraged wearable sensor technology for continuous monitoring of physiological signals in 16 healthy participants over two weeks. By systematically removing circadian and longer-scale rhythms, we isolated ultradian dynamics and modeled them using the Hankel Alternative View of Koopman (HAVOK) framework, a data-driven approach based on Takens' embedding theorem and Koopman operator theory. This allowed us to characterize ultradian rhythms as an intermittently forced linear system and distinguish between regular oscillatory behavior and more complex dynamics. Across participants, ultradian fluctuations were well-described by the HAVOK model, with intermittent forcing consistently observed. The model demonstrated strong forecasting accuracy, with root mean squared error (RMSE) of 0.0315 ± 0.02, 0.0306 ± 0.02, and 0.0218 ± 0.02 in the leading time-delay coordinates. Notably, a significant sex difference in model rank (z = -2.06, p = 0.0396) suggests that sex hormones may play a key role in ultradian dynamics. These findings provide evidence for intermittently forced linear systems as a useful framework for understanding ultradian rhythms and their regulation.Clinical relevance- Disruptions in ultradian rhythms are linked to neurological and psychiatric disorders. Identifying their key driver dynamics could inform chronotherapy and biomedical interventions, offering new strategies for regulation in health and disease.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing Chronic Kidney Disease Diagnosis Using Molecularly Imprinted Polymer-Integrated MEMS Diagnostic Technology for Precision Point-of-Care Detection and Management.","authors":"Sumedha Nitin Prabhu, Gouzhen Liu","doi":"10.1109/EMBC58623.2025.11252630","DOIUrl":"10.1109/EMBC58623.2025.11252630","url":null,"abstract":"<p><p>Chronic Kidney Disease (CKD) represents a major global health challenge, requiring innovative diagnostic solutions to enhance early detection and disease management. This study presents a novel approach integrating Molecularly Imprinted Polymer (MIP) technology with Electrochemical Impedance Spectroscopy (EIS) to develop a highly specific and cost-effective biosensor for creatinine detection. Creatinine, a key biomarker for renal function, serves as the target molecule, with MIPs offering exceptional specificity and stability in diverse analytical conditions. Employing advanced techniques such as Ultra-High Performance Liquid Chromatography (UHPLC), the structural and functional properties of the biosensor were meticulously characterized, demonstrating superior performance compared to Non-Molecularly Imprinted Polymers (NIPs). The biosensor's ethical and clinical relevance was ensured by using heat-inactivated human serum samples, aligning with the need for practical and scalable diagnostic tools. The developed MEMS biosensor-based Point-of-Care (PoC) device exhibited remarkable sensitivity, with a detection range from 6 ppm to 15 ppm, surpassing typical physiological creatinine concentrations. This facilitates real-time monitoring of CKD progression, addressing limitations of traditional diagnostic methods, such as high costs, time inefficiency, and inaccessibility. By revolutionizing PoC diagnostics, this work underscores the potential of MIP-based biosensors in advancing kidney healthcare. The findings pave the way for impactful applications in clinical diagnostics, offering an efficient, scalable, and transformative solution to CKD management.Clinical Relevance- The clinical relevance of this work lies in its ability to provide a rapid, cost-effective, and highly specific diagnostic tool for CKD. By integrating MIPs with MEMS technology, the biosensor enables the PoC diagnostic device to allow real-time creatinine monitoring, which is crucial for early-stage detection and disease management. This approach not only addresses the limitations of conventional diagnostics but also empowers healthcare providers with actionable data, potentially improving patient outcomes and reducing the burden of CKD progression.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"X2Graph for Cancer Subtyping Prediction on Biological Tabular Data.","authors":"Tu Bui, Mohamed Suliman, Aparajita Haldar, Mohammed Amer, Serban Georgescu","doi":"10.1109/EMBC58623.2025.11253574","DOIUrl":"10.1109/EMBC58623.2025.11253574","url":null,"abstract":"<p><p>Despite the transformative impact of deep learning on text, audio, and image datasets, its dominance in tabular data, especially in the medical domain where data are often scarce, remains less clear. In this paper, we propose X2Graph, a novel deep learning method that achieves strong performance on small biological tabular datasets. X2Graph leverages external knowledge about the relationships between table columns, such as gene interactions, to convert each sample into a graph structure. This transformation enables the application of standard message passing algorithms for graph modeling. Our X2Graph method demonstrates superior performance compared to existing tree-based and deep learning methods across three cancer subtyping datasets.Clinical relevance- This work advances the application of deep learning solutions to cancer diagnosis, particularly in scenarios where only limited tabular data is available.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145672634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Centimeter Differences in Wrist Electrode Placement Significantly Impact Myoelectric Performance.","authors":"Connor D Olsen, Samuel R Lewis, Joshua D Gubler, Mason K Coleman, Tyler S Davis, Jacob A George","doi":"10.1109/EMBC58623.2025.11253120","DOIUrl":"10.1109/EMBC58623.2025.11253120","url":null,"abstract":"<p><p>The long-term goal of this research is to establish electromyography (EMG) as an intuitive and dexterous control interface for human-computer interaction. EMG is an established technique for classifying hand gestures and motions, used often in prosthetics and orthotics. Recently, there has been a shift towards recording EMG at the wrist, instead of at the forearm, to yield a more socially acceptable form factor for consumer applications. EMG within the size of a watch or bracelet means fewer electrodes and more variable placement with respect to the underlying muscle anatomy. Here, we explore how differences in location along the wrist impact EMG quality and myoelectric control. We recorded EMG and compared myoelectric performance across three different regions of electrodes (distal, central, and proximal) using electrode arrays at both the wrist and the forearm. We found that a small 4.3 cm shift proximally on the wrist yields significant improvements in EMG information content and myoelectric performance. When trained on a k-Nearest Neighbors model, classification accuracy increased from 79.3% at the distal wrist region to 83.7% at the proximal wrist position. EMG from the proximal wrist region also had significantly more information content, as indicated by greater variance outside of the first principal component and by more frequently selected channels via a minimum-redundancy-maximum-relevance selection approach. These findings indicate that the spatial position of electrodes at the wrist has a noticeable impact on myoelectric control in a way not seen in traditional EMG recordings from the forearm. This can inform the design of future wrist-worn EMG devices, which in turn may lead to more robust control for partial hand prostheses, hand orthoses, and augmented/virtual reality.Clinical Relevance- A subtle change in the position of electromyographic electrodes on the wrist can yield significant improvements in the control of technology, like prostheses, exoskeletons, and virtual/augmented reality.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2025 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12740639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}