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":"Development of rplec: An R package of placental epigenetic clock to estimate aging by DNA-methylation-based gestational age.","authors":"Herdiantri Sufriyana, Emily Chia-Yu Su","doi":"10.1109/EMBC58623.2025.11254137","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254137","url":null,"abstract":"<p><strong>Background: </strong>Latest placental epigenetic clocks (PlECs) were less accurate in earlier trimesters. We aimed to develop an R package of PlEC to estimate aging by DNA-methylation-based GA (DNAm-GA).</p><p><strong>Methods: </strong>We developed a three-stage prediction model, including residual DNAm-GA for measuring placental aging. An R package was developed to simplify our scikit-learn models into a single function and to utilize DNAm-GA for placental aging study.</p><p><strong>Results: </strong>Our PlEC achieved a lower root mean squared-error for preterm samples (0.558, 95% confidence interval [CI] 0.545, 0.570) compared to the two previous PlECs using the common dataset: (1) Lee et al. (1.696, 95% CI 1.667, 1.724); and (2) Mayne et al. (4.018, 95% CI 3.927, 4.108). We also provided a function to utilize DNAm-GA for placental aging study.</p><p><strong>Conclusions: </strong>Our R package precisely estimated DNAm-GA and our analytical framework could utilize DNAm-GA for placental aging study.Clinical Relevance- Our placental epigenetic clock allows individual assessment of placental aging in clinical settings via the residual DNAm-GA.</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":"145671591","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":"Convolutional Neural Networks for Seizure Detection: A Study on Training Strategies.","authors":"David H Agustsson, Steinn Gudmundsson","doi":"10.1109/EMBC58623.2025.11253676","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253676","url":null,"abstract":"<p><p>This study investigates how various training strategies originally proposed in the context of image processing, can be used to improve the performance of a convolutional neural network designed for classification of seizures from EEG recordings.Random cropping of seizure segments, dropout, mixup and ensembling improved the performance of the baseline classifier, alone and in combination. The best results were obtained by a combination of random cropping, mixup and ensembling, improving the AUC from 0.957 to 0.981 and F1-score from 71.0% to 77.9%.This study shows the importance of optimizing the training of neural networks for seizure detection.</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":"145671592","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":"Decoding Visual Imagination and Perception from EEG via Topomap Sequences.","authors":"Hossein Ahmadi, Luca Mesin","doi":"10.1109/EMBC58623.2025.11251641","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251641","url":null,"abstract":"<p><p>We propose a Topomap-based EEG decoding framework for distinguishing pictorial Imagination from Perception. By converting each trial's EEG signals into dense sequences of scalp voltage maps at short time intervals, our approach captures crucial spatiotemporal patterns that standard methods may overlook. We then apply a CNN with squeeze-and-excitation (SE) blocks to these Topomap \"frames,\" enabling direct learning of both spatial topographies and rapid temporal fluctuations. Despite using only one trial per subject to simulate a data-scarce scenario, our model achieves 95.1% accuracy under a leave-one-subject-out (LOSO) cross-validation scheme. Results indicate clear neural distinctions between Imagination and Perception states, reflecting focused brain-region engagement during visual recall. In addition to confirming the viability of Topomaps as EEG feature representations, this study underscores their potential generalizability. We anticipate future extensions incorporating other modalities (orthographic, audio) and more advanced deep architectures will further expand the utility and robustness of this approach for brain-computer interface (BCI) applications.Clinical relevance- This framework offers a robust method for accurately distinguishing visual Imagination from Perception, even in data-scarce scenarios. It holds potential for enhancing diagnostic tools in cognitive disorders and refining BCI applications in clinical settings.</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":"145671523","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":"Behavioral and Psycho-Emotional mHealth Interventions for Elderly Breast Cancer Patients with Cardiac Toxicity<sup />.","authors":"Maria E Chatzimina, Georgia S Karanasiou, Ketti Mazzocco, Gabriella Pravettoni, Gaia Giulia A Sacco, Maria A Toli, Andri Papakonstantinou, Athos Antoniades, Nectaria Chrysanthou, Anastasia Constantinidou, Vassilis Bouratzis, Daniela M Cardinale, Gerasimos Filippatos, Kalliopi Keramida, Dorothea Tsekoura, Domen Ribnikar, Kostas Marias, Dimitrios I Fotiadis, Manolis Tsiknakis","doi":"10.1109/EMBC58623.2025.11253427","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11253427","url":null,"abstract":"<p><p>The CARDIOCARE project combines psycho-emotional and behavioral interventions into a mobile health (mHealth) application for the elderly breast cancer patients who have a risk for therapy-induced cardiac toxicity. The mHealth application includes psycho-emotional modules such as Expressive Writing, the ABCDE Model, and Best Possible Self alongside behavioral interventions like prompted voiding and pelvic floor exercises, which are focused on both improving psychological resilience and physical health. By focusing on both physical and psychological needs, the app aims to improve patient adherence to treatment and to alleviate healthcare burden. Preliminary results based on data analysis collected from 67 patients from six clinical centers show promising trends: 45% of patients initially expressed denial in their first entries using the ABCDE module, which later shifted towards acceptance and active coping strategies. These findings show the potential of the CARDIOCARE interventions to enhance the well-being in elderly cancer patients. Ongoing trials are expected to provide a more comprehensive understanding of these interventions and their impact on improving psychological well-being and overall quality of life of cancer patients.Clinical Relevance- This study investigates the potential of the CARDIOCARE mHealth application to address both psychological and physical needs in elderly cancer patients with therapy-induced cardiac toxicity. Preliminary results from six clinical centers indicate that the CARDIOCARE mHealth application can support elderly cancer patients by helping them to express their emotions, cope with their illness, and adopt healthy routines. These interventions could help clinicians enhance patient care by providing personalized support and remote monitoring, resulting in better quality of life outcomes.</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":"145671549","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":"Automatic classification of idiopathic Parkinson's disease using kinematic data of motion capture systems.","authors":"J A Gomez-Garcia, A Torres-Pardo, C Trigo-La Blanca, M Algaba-Vidoy, V Navarro-Lopez, D Fernandez-Vazquez, P Molero-Mateo, M Carratala-Tejada, F Molina-Rueda, D Torricelli","doi":"10.1109/EMBC58623.2025.11252917","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252917","url":null,"abstract":"<p><p>Idiopathic Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects the human nervous system. PD is the second most common neurodegenerative disorder worldwide, affecting more than 10 million people. Despite its high prevalence, the diagnosis still relies on subjective assessments that examine motor performance through clinical scales. The use of sensor-based technologies offers a promising opportunity to improve diagnosis processes as it might make the disease progression quantifiable and more objective. This paper presents a methodology for the automatic detection of PD using kinematic data in a novel database that is being recorded for the analysis of this disorder using motion capture technologies. Experiments are carried out with 37 patients with PD and 15 healthy subjects wearing Inertial Measurement Units (IMU) and photogrammetry systems while walking on flat terrain. Five different classification systems, including a novel transformer-based foundational model for tabular data (TabFPN), were used to discriminate between healthy controls and patients with PD and compared to more classical and tabular-based classification algorithms. The results indicate the abilities of TabFPN in automatically discriminating between PD and controls, reaching an accuracy of up to 78% and a ROC-AUC of 89%.</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":"145671560","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":"Combined Anomaly Features and Interictal Epileptiform Discharges for Effective Seizure Onset Zone Localization.","authors":"Nawara Mahmood Broti, Masaki Iwasaki, Yumie Ono","doi":"10.1109/EMBC58623.2025.11254946","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254946","url":null,"abstract":"<p><p>Epilepsy, a prevalent neurological disease, often requires accurate identification of the seizure onset zone (SOZ) in the brain for successful surgical removal of this region. SOZ identification is a lengthy process that traditionally relies on the knowledge and experience of the neurosurgeon. Advancements in artificial intelligence have opened the door to automatic SOZ localization. This study proposes the application of autoencoder-based anomaly detection in SOZ electrode classification for the first time. We trained the autoencoder in a leave-one-patient-out manner with electrocorticography (ECoG) signals from intact channels. The anomaly feature was determined by the maximum error between original and reconstructed signals for each channel of the test patient. We investigated the usefulness of anomaly features along with the interictal epileptiform discharge (IED) biomarker feature. A linear support vector machine classifier achieved 70.49% accuracy with the anomaly feature, 64.71% accuracy with IED feature, and 74.44% accuracy with combined anomaly and IED features. The study demonstrates the effectiveness of anomaly detection in the direct localization of SOZs and suggests that multiple biomarker features can enhance automatic SOZ localization performance.Clinical Relevance- This study demonstrates the clinical relevance of using anomaly features from ECoG data for efficient SOZ localization. It highlights the effectiveness of combining anomaly features with IED biomarkers to enhance the automatic SOZ classification performance and improve surgical planning in epilepsy treatment.</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":"145671562","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":"Dose-Dependent Regional Synchronicity Changes Using Weighted Phase Lag Index: Towards Optimizing Vagus Nerve Stimulation Titration Process.","authors":"Paloma Carcamo Cerda, Pablo Aqueveque, Antoine Nonclercq, Riem El Tahry, Enrique Germany","doi":"10.1109/EMBC58623.2025.11254022","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254022","url":null,"abstract":"<p><p>Vagus Nerve Stimulation (VNS) is an effective therapy for drug-resistant epilepsy (DRE), but its efficacy varies across individuals. This study uses the weighted Phase Lag Index (wPLI) across multiple frequency bands to examine the dose-dependent regional brain synchronization effect at an individual level. EEG data from four DRE patients (two responders and two non-responders) were analyzed under controlled VNS intensities. Results show that non-responders exhibit an increased synchrony at higher intensity levels, particularly in the beta band and the broadband. Findings highlight individualized neural responses to VNS, underscoring the possibility for personalized stimulation strategies to optimize therapy.Clinical Relevance- Refining the VNS titration process through dose-dependent brain connectivity analysis could accelerate optimization, improving therapeutic outcomes and personalization for drug-resistant epilepsy patients.</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":"145671646","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":"Deep-Meg: A deep learning approach for magnetoencephalograhic inverse problem solutions.","authors":"Stefano Franceschini, Michele Ambrosanio, Maria Maddalena Autorino, Fabio Baselice","doi":"10.1109/EMBC58623.2025.11251761","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11251761","url":null,"abstract":"<p><p>This manuscript introduces a deep learning algorithm designed for spatial and temporal source reconstruction based on signals captured by MEG devices. Estimating brain signals at the source level is a significant challenge in magnetoencephalographic (MEG) data processing. Traditional algorithms excel in temporal resolution but face limitations in spatial resolution due to the inherently ill-posed nature of the problem. However, precise localization of pathological tissues is often crucial for providing reliable information to clinicians, which makes this a key area for improvement. Deep learning solutions have emerged as promising candidates for high-resolution signal estimation in this context. The proposed approach, called 'Deep-MEG', utilizes a hybrid neural network architecture capable of extracting both temporal and spatial information from MEG sensor signals. Unlike traditional methods, the algorithm can handle the entire brain, making it suitable for imaging not just cortical sources but also subcortical ones. To validate its performance, the authors conducted simulations with multiple active sources using a realistic forward model and compared the results with those from various state-of-the-art reconstruction algorithms.Clinical relevance- This study represent a first approach for accurate deep source localization and reconstruction leading to diagnosis support to clinicians.</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":"145671648","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":"Enhancing Colon Cancer Risk Prediction in Machine Learning Models using Polygenic Risk Scores.","authors":"Kyungbeom Kim, Harinishree Sathu, Andrew Hornback, Monica Isgut, Joshua Traynelis, May D Wang","doi":"10.1109/EMBC58623.2025.11252666","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11252666","url":null,"abstract":"<p><p>Colon cancer is one of the deadliest types of cancer in the United States, with close to 50,000 projected deaths in 2024. The disease requires early diagnosis to optimize chances of survival by enabling timely administration of treatment. To investigate the key non-genetic (NG) factors influencing the onset of colon cancer and evaluate how genetic factors enhance the performance of machine learning (ML) models in predicting incidence, we incorporated polygenic risk scores (PRSs) alongside NG data in ML models to predict 10-year incident risk prediction of colon cancer using data from the UK Biobank. This approach enabled us to assess the added predictive value of PRSs in multi-modal models in estimating the 10-year risk of developing colon cancer over NG data alone. Moreover, our research focused on identifying the most relevant and predictive PRS and validating them using a robust ML framework. To ensure the robustness, we restricted the cohort to White British individuals to minimize ancestry-related heterogeneity. PRSs have proven effective in enhancing disease prediction for conditions such as breast cancer, myocardial infarction, and schizophrenia, reinforcing their relevance in clinical research. Exploring six PRSs, our goal was to minimize false negatives while simultaneously maximizing area under the receiver-operating characteristic curve (AUC), in order to improve early detection rates by identifying those who are at risk for colon cancer. This research shows that PRSs can be used to enhance overall predictive ability of ML models in colon cancer research over NG factors alone, bolstering the argument for incorporating PRSs into routine clinical practice. PRSs can also help minimize false negatives, a key feature for disease prediction models, as missed potential diagnoses are life-threatening.</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":"145671639","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":"Diffusion Model-Based Displacement Field Generation for 4D-CT Chest Image Generation.","authors":"Miki Kanamuro, Hideaki Hirashima, Mitsuhiro Nakamura, Megumi Nakao","doi":"10.1109/EMBC58623.2025.11254765","DOIUrl":"https://doi.org/10.1109/EMBC58623.2025.11254765","url":null,"abstract":"<p><p>Acquisition of data on respiratory motion and anatomical deformation at the individual level is essential for improving the accuracy of surgical procedures and radiotherapy. Although time-series imaging using three-dimensional (3D) computed tomography (CT) and deep learning-based image interpolation have been explored, the acquisition of multiple imaging volumes remains a significant burden on patients because of breath-holding requirements and additional radiation exposure. In this study, we propose a framework for four-dimensional (4D) CT image generation, with images being generated at different time phases from a single-phase 3D chest CT image using only the magnitude of displacement. To achieve this, we employ a conditional diffusion model to generate displacement vector fields (DVFs) and propose a model that incorporates the initial-phase CT image and the mean DVF of the target phase as guidance. We trained and tested the proposed model using 4D-CT images from 62 cases and evaluated its effectiveness by deforming the 3D-CT image at the end-expiration phase using the predicted DVF. The validity of our approach was confirmed through quantitative comparisons under multiple guidance scenarios.Clinical Relevance- The proposed diffusion model predicts Deformation Vector Fields (DVFs) that capture respiratory motion, thereby enabling the generation of 3D-CT images at different respiratory phases from a single-phase CT scan. This approach could be directly applied to radiotherapy planning and we expect that it could improve radiation targeting accuracy.</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":"145671630","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}