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

Infants born prematurely, or preterm, can experience altered brain connectivity, due in part to incomplete brain development at the time of parturition. Research has also shown structural and functional differences in the brain that persist in these individuals as they enter adolescence when compared to peers who were fully mature at birth. In this study, we examined functional network energy across multiscale functional connectivity in approximately 4600 adolescents from the Adolescent Brain Cognitive Development (ABCD) study who were either preterm or full term at birth. We identified three key brain networks that show significant differences in network energy between preterm and full-term subjects. These networks include the visual network (comprising the occipitotemporal and occipital subnetworks), the sensorimotor network, and the high cognitive network (including the temporoparietal and frontal subnetworks). Additionally, it was demonstrated that full-term subjects exhibit greater instability, leading to more dynamic reconfiguration of functional brain information and increased flexibility across the three identified canonical brain networks compared to preterm subjects. In contrast, those born prematurely show more stable networks but less dynamic and flexible organization of functional brain information within these key canonical networks. In summary, measuring multiscale functional network energy offered insights into the stability of canonical brain networks associated with subjects born prematurely. These findings enhance our understanding of how early birth impacts brain development.

We present a novel topological framework for analyzing functional brain signals using time-frequency analysis. By integrating persistent homology with time-frequency representations, we capture multi-scale topological features that characterize the dynamic behavior of brain activity. This approach identifies 0D (connected components) and 1D (loops) topological structures in the signal's time-frequency domain, enabling robust extraction of features invariant to noise and temporal misalignments. The proposed method is demonstrated on resting-state functional magnetic resonance imaging (fMRI) data, showcasing its ability to discern critical topological patterns and provide insights into functional connectivity. This topological approach opens new avenues for analyzing complex brain signals, offering potential applications in neuroscience and clinical diagnostics.

Electroencephalography (EEG) provides millisecond-scale resolution of neural activity but struggles to accurately localize multiple concurrent sources, especially in spatially close regions. Classical linear inverse methods, such as MNE, sLORETA, and dSPM, address the ill-posed inverse problem through regularization but often exhibit a "single-source bias", suppressing smaller generators. This paper introduces a deep learning framework designed to robustly identify multiple sources of activity from short EEG segments. Our approach leverages a realistic simulation pipeline that systematically generates EEG recordings from physiologically plausible, distributed current sources. We train a convolutional neural network (ConvNET) on thousands of such simulations, ensuring generalization by using a forward model distinct from that of classical solvers, thereby minimizing the risk of an "inverse crime". We evaluate our ConvNet against nine well-established inverse solvers (MNE, dSPM, sLORETA, eLORETA, LORETA, LAURA, and depth-weighted variants). Benchmarking across multiple synthetic test scenarios demonstrates that our method consistently outperforms traditional solvers, particularly in resolving closely spaced sources, while maintaining or improving accuracy for single-source cases. These results highlight the potential of deep learning to overcome biases in EEG source imaging, offering a more reliable approach for multi-source localization.Clinical relevance- By leveraging deep learning, our approach improves localization accuracy, particularly in closely spaced or deep brain sources, potentially enhancing presurgical planning, brain-computer interfaces, and real-time neurofeed-back applications.

Characterization of drug-induced changes in the cancerous cells is important in improving the efficacy of chemotherapeutic drugs and for personalized medicine. This study analyzes the morphological changes in the nuclei objects of cells treated with the drugs targeting Aurora Kinase (AURK) gene family. For this, fluorescence images of lung cancer cell line treated with AMG900 are obtained from a publicly available database. The images are pre-processed and segmented to separate the nuclei objects from the background. Nuclear boundaries are detected, and various shape descriptors, including eccentricity, circularity, convexity, bending energy, and area are computed to comprehensively analyze the drug-induced changes in nuclear morphology. The obtained results show that the bending energy demonstrated high consistency and sensitivity in capturing nuclei irregularities compared to other shape-based metrics, with the highest mean value of 6.71. Nuclei object with a maximum value of bending energy 8.69 exhibit significant boundary variations with increased area and a minimum value of 2 with smooth curvatures. The statistical analysis of the bending energy variations across four replicates resulted in mean bending energies of 6.7, 6.8, 6.5, and 6.5 which indicates the replicate matching morphologies with confirmed reproducibility. Thus, bending energy has proved to be an effective and reliable parameter for measuring the nuclear membrane irregularities in lung cancer cell lines due to chemical or genetic perturbations.Clinical relevance- This irregularity measure can be employed for biocompatibility testing in the standardization of biomedical devices.

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.

Punch biopsy is a skin biopsy method that is used to remove a small sample of the epidermis and dermis. The procedure requires a trained dermatologist to excise the sample with a punch biopsy tool and then immediately apply sutures for wound closure. Punch biopsy is a common procedure necessary for the diagnosis of many conditions. Thus, the present shortage of dermatologists-especially in the developing world-motivates the design of convenient methods for punch biopsy that do not require clinical training. Here, we present a medical device that streamlines punch biopsy and wound closure into simple, sequential operations. The handheld device engages and securely locks the skin, excises the biopsy sample, then applies a N-butyl cyanoacrylate tissue adhesive for wound closure. The device is validated ex vivo using porcine ear skin, which has comparable biomechanical properties to human skin. For engagement, the device can target the desired biopsy area with an accuracy of 2 mm. For sample collection, the device reliably excises samples 7 mm in diameter and 4 mm in depth. For wound closure, the device streamlines the application of tissue adhesive to seal the wound, albeit with less strength than surgical sutures. Altogether, these results validate the design of a device for punch biopsy and wound closure that can be used within sequential steps with minimal training.

Studying the interplay between respiration patterns and the heart rate variability (HRV) through mathematical models led to valuable insights into autonomic nervous system (ANS) functioning. Despite several models have been proposed in the literature, there is a lack of a general mathematical framework based on systems theory and formulated according to a rigorous control theory formalism. This work aims to reframe existing cardiopulmonary models into a general finite-dimensional nonlinear time-invariant (FDNTI) framework to capture respiration-cardiovascular interactions. A MATLAB Simulink-based implementation is presented and a simulation study is carried out. By exploiting control theory formalism, a system theoretic oriented model is obtained, which addresses roles of state variables, inputs, and linear/nonlinear contributions. Simulation tests confirmed the validity of the proposed modeling approach. This generalized formulation could enable in-depth analysis of physiological and pathological states by adopting advanced control theory techniques to investigate stability properties of the cardiorespiratory system.Clinical relevance- An in-silico model of respiration-cardiovascular interactions for assessing autonomic functioning.

Current research on steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) predominantly focuses on utilizing the frequency- and phase-locking characteristics of SSVEP for encoding purposes. In this study, we propose an innovative paradigm wherein SSVEP serves as a marker, integrated with different types of motion animations to identify distinct neural processing pathways associated with these animations. This approach enables the classification of SSVEP-based BCIs without relying on frequency features. We designed six distinct animations corresponding to six behaviors commonly observed in daily life. Each animation was tagged with a uniform 6 Hz stimulus frequency, forming a six-target classification task. Offline testing was conducted with 10 participants. Despite identical frequency components, significant differences in spatial distribution corresponding to the animations were observed, likely due to the behavioral variations in the animations. Classification analysis demonstrated an accuracy of 0.93 within a 6-second window, validating the practical feasibility of this approach. This paradigm offers a novel direction for the advancement of SSVEP-based BCIs, potentially enabling the integration of multi-sensory information.

Automatic sleep staging typically requires multi-channel EEG data, limiting its application in portable devices. To address this, we propose a hybrid deep learning model that utilizes multi-domain features from single-channel EEG data collected via polysomnography (PSG). Our model employs two feature extractors to capture time-domain and time-frequency-domain features, which are fused for final predictions. Validated on the Haaglanden Medisch Centrum Sleep Centre Database (HMC) with EEG data from 151 subjects, the model achieves an accuracy of 0.747 and an F1 score of 0.742. Compared to state-of-the-art methods, it shows improved multi-classification performance, particularly in N3 stage detection. This study highlights the potential of single-channel EEG for accurate sleep staging and the development of portable PSG-based monitoring systems.Clinical Relevance-This study develops a deep learning model for automatic sleep staging only using a single-channel EEG. Our research would be helpful to automatically classify stages during sleep for sleep physicians.

Sleep stage classification is a critical task in sleep research, with significant implications for diagnosing and treating sleep disorders. Traditional methods rely on manual scoring of polysomnography (PSG) data, which is time-consuming and prone to human error. While recent advances in deep learning have enabled automated sleep stage classification, challenges persist in handling the complex, non-linear patterns of physiological signals. Existing models are often computationally expensive, require sophisticated feature extraction methods, and are unsuitable for real-time implementation. To address these limitations, we propose a lightweight and efficient dual-branch deep-learning model that leverages the feature extraction capabilities of CNNs and the channel-wise attention mechanisms of Transformers. Unlike conventional transformers, it avoids excessive computational complexity while effectively capturing both local and global dependencies in physiological signals. The model is validated on four benchmark datasets-SleepEDF-20, SleepEDF-78, SleepEDFx, and SHHS-and demonstrates superior performance compared to several baseline algorithms. Our proposed algorithm achieves state-of-the-art results across all datasets, highlighting its robustness and scalability for real-world applications. The code for the proposed algorithm is publicly available at link, enabling reproducibility and further research. Combining the strengths of CNNs and Transformers, it offers a promising solution for accurate and efficient sleep stage classification, paving the way for improved diagnosis and treatment of sleep disorders. The code is available at https://github.com/ang-frozen/embc2025.