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

Epilepsy affects approximately 50 million individuals worldwide, with nearly one-third suffering from drug-resistant epilepsy (DRE). For these patients, localizing the epileptogenic zone (EZ) is critical for effective surgical intervention but often requires implantation of intracranial electrodes and days to weeks in the hospital to record seizures. This study evaluates the efficacy of neural fragility, a dynamical network-based metric, as a computational biomarker for the identification of epileptogenic nodes during resting-state intracranial EEG (iEEG). Because EZ can never be truly validated in human iEEG data due to the absence of ground truth, we use in-silico data with pre-defined EZs, generated with a phenomenological network model, to assess the predictive accuracy of neural fragility in localizing seizure-generating regions. Results demonstrate a bimodal distribution of fragility scores, with a threshold-based classification accurately identifying epileptogenic nodes in 45% and 54% of simulations for two different datasets. While findings highlight the potential of neural fragility for EZ localization, variability in predictions suggests a need to determine physical and phenomenological factors driving prediction accuracies. Future work will focus on parameter optimization of dynamical network models, integration of additional network features, and validation of the model with clinically derived (iEEG) data that include surgical success results.Clinical Relevance- This research advances computational methods for epilepsy surgical planning, aiming to improve patient outcomes through more precise epileptogenic zone localization.

One of the leading causes of morbidity and mortality in the world is Traumatic Brain Injury (TBI). Different outcomes are influenced by regional access and health infrastructure. In this study, using 17 predictor variables, we evaluate machine learning models performance and generalizability with two different datasets of Brazilian regions. The first region is Manaus, an isolated urban center with differentiated logistical challenges. The second, is São Paulo, an urban center. To the best of our knowledge, this study is the first one that evaluate predictive models in two distinct datasets in the same country. In the results obtained with 1-D convolutional neural network (CNN) models, the area under the ROC curve (AUC) in São Paulo and Manaus were 0.90 and 0.93, respectively. The model trained in São Paulo does not perform well in Manaus. The incorporation of context-specific features, such as time between trauma and admission, and pandemic-related variable significantly increased the model's accuracy in Manaus model, achieving a remarkable AUC of 0.98.Clinical Relevance- We highlighted the necessity of integrating local variables to improve TBI prediction in different healthcare environments.

Brain-Heart Interplay (BHI) research is gaining increasing attention in the scientific community. However, the complexity and time-varying nature of BHI pose significant methodological challenges linked to the numerous variables involved, including directionality (i.e., descending brain-to-heart and ascending heart-to-brain), oscillatory dynamics, and scalp locations. It remains unclear whether the spatio-temporal variability of BHI can be effectively captured by reducing the dimensionality of the problem. In this study, we leverage a principal component analysis (PCA)-based approach to investigate the existence of a synergistic BHI. Experimental results on a publicly available EEG-ECG dataset of healthy subjects in resting state confirm the existence of principal components in BHI dimensions, highlighting distinct characteristics based on directionality and oscillatory frequency.Clinical relevance: The proposed methodology could provide novel biomarkers to support the diagnosis of neurological, psychiatric, and cardiovascular disorders.

This study evaluates the potential of a serious game, called Barn Ruins, as a spatial learning rehabilitation tool for older adults with mild to moderate cognitive impairment (MCI). The game's user navigates a maze environment on a laptop screen using a gaming controller (a joystick). It progresses through easy, medium, and hard routes, and has an error-based spatial learning score. The intervention spanned eight weeks, during which participants played the game for 30 minutes, three times a week. Pre-and post-intervention assessments were conducted using two independent and validated spatial orientation measures: VRNHouse as the primary and the Clock Orientation Test as the secondary outcome.Seven participants (86.3 ± 4.9 years, 2 males) completed the study. Although no statistically significant changes were observed in VRNHouse or Clock Orientation Test scores, 71.4% of participants improved or maintained their performance in the primary outcome measure, while 66.7% demonstrated improvement or stability in the secondary measure. Analysis of spatial learning scores within the Barn Ruins game revealed significant improvements over time (p = 0.0046, Kendall's W = 0.42), particularly in easy (p = 0.023) and hard (p = 0.01) routes. Performance on medium routes fluctuated, suggesting greater difficulty with these trials.Post-hoc comparisons revealed that by Weeks 7 and 8, participants' overall spatial learning scores were significantly higher compared to those in Week 1. Notably, easy routes exhibited a ceiling effect after Week 4, while harder routes showed consistent improvement after Week 5.Despite modest results in independent outcome measures, the game's significant performance gains suggest its utility in improving spatial skills. Future research with larger samples is needed to validate these findings.Clinical Relevance- These findings highlight the potential of the Barn Ruins game as a novel rehabilitation tool for enhancing spatial learning in older adults with MCI.

Patients with spasticity require intensive rehabilitation to regain motor function, but access is often limited by travel or healthcare constraints. Telerehabilitation provides a structured approach for home-based therapy, allowing patients to perform more exercises with minimal external assistance. This work presents a telerehabilitation platform for a compliant upper-limb robotized orthosis to support rehabilitation in both home and institutional settings with minimal setup required. Built using the OpenTera framework for rapid prototyping and modular service integration, the platform includes a patient user interface for guided exercises, progress tracking, and real-time feedback. A motor control module assists movement based on therapist-prescribed parameters, while role-based access control ensures secure data management for healthcare professionals.

Background: 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).

Methods: 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.

Results: 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.

Conclusions: 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.

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.

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.

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.

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%.