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

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.

Pharmacokinetics-Pharmacodynamics (PK/PD) data analysis is a cornerstone of both drug development and efficacy and safety studies. However, individual-level PK/PD data are difficult to obtain, expensive, and scattered throughout different clinical trials, for which usually only aggregated statistics are publicly reported. Meta-Analysis (MA) approaches from simple MA to the more advanced multi-variate meta-regression, and Model-Based MA (MBMA) are among the available tools to interpret average-level data. Ideally, the availability of individual patient data (IPD) would allow methods based on parametric pharmacological models, such as MBMA, to provide a better characterization of the relationships between covariates and PK/PD parameters. We propose to leverage a generative-AI approach to regenerate the IPD data of cohorts with only population-level statistics, by exploiting the availability of a small set of IPD.To test the methodology, we simulate a scenario with different datasets related to different clinical studies. The generative model is trained using IPD from a single study and can then generate IPD data from the population statistics of all others. We show that our algorithm can successfully learn and apply the original relationships of the IPD study to regenerate information lost by averaging data for external reporting purposes. In order to validate and test the analysis, we carried out performance tests showing a good agreement between model-simulated ground truth data and ML-generated data.

The complexity of biological systems and the limitations of analyzing individual omics studies for biomarker discovery have raised the need for a holistic approach by multi-omics integration. By integrating data from multiple layers, researchers can gain insights into the entire system rather than just individual components. Also, integrative analysis can help identify molecular signatures that are more accurate in predicting disease onset, progression, and response to treatment, leading to better-targeted therapies and personalized medicine. In this paper, we explored statistical and deep learning methods for integrative analysis of metabolomics, lipidomics, peptidomics, proteomics, and glycoproteomics data acquired by LC-MS/MS analysis of serum samples from 20 hepatocellular carcinoma (HCC) cases and 20 patients with liver cirrhosis (CIRR). The goal is to identify a panel of multi-omics features that distinguish HCC cases from cirrhotic controls. A pathway analysis using these features identified biological pathways such as LXR/RXR Activation and Acute Response signaling as significantly enriched in our multi-omics datasets.

Minimally invasive procedures for diagnosing and treating occlusive arterial diseases and prostate cancer face significant challenges due to the complexity of navigating within occluded arteries and precisely positioning surgical needles. Fiber optic sensors, coupled with optical frequency domain reflectometry (OFDR), offer promising solutions to the accuracy limitations of traditional imaging methods in complex anatomies. This work proposes custom calibration techniques of fiber optic sensors for vascular catheters and prostate surgical needles, addressing device-specific characteristics that can cause shape sensing inaccuracies, making precise and reliable calibration crucial. We assessed how calibration, tool characteristics, and spatial resolution affect shape reconstruction accuracy, with the catheter calibration protocol yielding a root-mean-squared-error (RMSE) of 1.67 ± 0.77 mm (0.4% ± 0.2%), and the needle calibration protocol achieving 0.23 ± 0.14 mm (0.2% ± 0.1%). Although the impact of spatial resolution wasn't significant, it's crucial to consider as it varies with the specific medical device and application.Clinical relevance-The proposed calibration methods enhance the safety and precision of fiber optic minimally invasive procedures by reducing reliance on imaging like fluoroscopy, minimizing tool placement errors across various medical devices and clinical domains. We demonstrate potential for automation to improve both clinical outcomes and workflow efficiency.

For powered lower-limb prostheses to be translated from research environments to real-world use, they must be able to perform a variety of daily activities, such as walking on level or ramped surfaces, stair climbing, sitting, and standing. The device must quickly and predictably switch between the modes corresponding to these activities. Multiple methods exist to trigger activity mode transitions, but they can overlook user agency, be slow and cumbersome to enact, lack discretion, or have limited predictability. This work presents a smartwatch application that allows the user to wirelessly control the activity mode of the prosthesis. The user can perform a swipe gesture on the smartwatch to transition to the desired mode, while the smartwatch provides vibrotactile haptic and visual feedback to the user to indicate the activity mode of the device. An experiment with one transfemoral amputee participant showed that the smartwatch application is viable for providing user control of the activity mode to traverse a multi-activity circuit using a powered knee-ankle prosthesis.

Transcutaneous electrical nerve stimulation (TENS) is an electrophysical non-invasive modality widely used for non-pharmacological pain management in several pain conditions, including labor pain. However, the efficacy of TENS in labor pain is yet not determined. Therefore, this study aimed to investigate the efficacy of TENS compared to sham in labor pain and examine the effective set of varying frequencies.

Methods: A double-blinded randomized sham-controlled pilot study was conducted in the labor ward at the Department of Obstetrics and Gynecology, Gødstrup Hospital, Herning Denmark. Healthy low-risk pregnant women admitted for childbirth at term were recruited and randomized to receive one of three types of TENS stimulation for 30 min; TENS1, which varied from low to high frequencies (4/100 Hz), TENS2 with high frequencies (80/100 Hz), and sham-TENS.

Results: Of 23 women eligible, 12 women agreed to participate. No efficacy of any type of varied frequencies in TENS compared to sham-TENS was shown. However, TENS1 showed a non-significant reduction in visual analogue scale (VAS) compared to sham with 1.9 ± 3.4 cm at 10 min and 1.0 ± 2.5 cm at 30 min. Further, pain pressure threshold (PPT) showed a slight increase in sensitivity for TENS1 with 18.2 ± 58.7 kPa from baseline to 10 min. This may suggest a short-term efficacy of 10 min of TENS.

Conclusion: Our findings suggest that a main study with a proper sample size based on this study investigating TENS1 may reveal if this set of frequencies reduces pain in laboring women.Clinical Relevance - This study suggests relevance of considering non-pharmacologic and neuromodulating approach in labor pain management.

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.