IEEE Transactions on Biomedical Engineering最新文献

Objective: Respiratory surface electromyography (sEMG) is a promising physiological signal for analyzing respiratory effort, patient-ventilator asynchrony, and respiratory training. In clinical research, a wide variety of different setups are used and no consensus has yet been reached on the positioning of electrodes. Therefore, this work aims to quantitatively compare both unilateral and bilateral bipolar electrode leads.

Methods: Recordings of diaphragmatic and intercostal muscle activity were performed in 20 young and healthy adults using a setup with 64 electrodes placed in relation to prominent anatomical lines. Subjects completed three breathing maneuvers: 300 s quiet breathing, 5 maximum inspiratory pressure (MIP) trials, and 15 breaths of resistance breathing at 20 % of the MIP. To quantify the performance of differential electrode leads, three metrics were determined: the ratio between inspiratory muscle activity and (1) baseline noise (SNR_base), (2) expiratory muscle activity (SNR_exp), and (3) ECG interference (SNR_EMG-ECG).

Results: The study revealed considerable differences between bipolar electrode positions. Our results support the use of bilateral positions on the midclavicular line and parasternal line for measuring diaphragm and intercostal activity. For intercostal muscles, there is a high flexibility in positioning electrodes more lateral or medial, if necessary. Unilateral leads do not appear to outperform the bilateral configuration as SNR metrics were consistently smaller.

Conclusion: This study provides recommendations for electrode placements and is a first step towards standardization of respiratory sEMG measurements.

Significance: This electrode lead standardization will be essential to increase clinical acceptance in the future.

Objective: Mitral regurgitation is a valvular heart disease particularly affecting the aging population. Minimally invasive transcatheter procedures offer benefits over traditional open-chest surgery but require significant operator skill and hand-eye coordination, making the learning curve steeper and limiting accessibility. To address these challenges, there is growing research interest in automating these procedures, making it crucial to define safe navigable routes within anatomical structures for robotic operation. This study introduces a tailored learning-based framework for path planning in cardiac percutaneous interventions, specifically adapted to the dynamically constrained and safety-critical environment of mitral valve repair.

Methods: We compared generative adversarial imitation learning and behavioral cloning techniques to traditional path planning algorithms like rapidly-exploring random trees. Using patient-specific anatomical data, a faithful digital twin was created, with dynamic motions to replicate real-time cardiac movements of the mitral valve.

Results: Learning approaches significantly reduced target position errors and improved path smoothness with greater clearance from obstacles compared to state-of-the-art methods.

Conclusion: Learning methodologies provided consistent and repeatable routes in cardiac anatomy, both in pre-operative static and intra-operative dynamic scenarios.

Significance: Embedding task demonstrations in the learning process shows the potential to automate and optimize catheter navigation, promoting standardization of minimally invasive cardiac procedures.

Vital signs are crucial clinical measures, with body temperature (BT) and heart rate (HR) being particularly significant. While their association has been studied in adults and children, research in Pediatric Intensive Care Unit (PICU) settings remains limited despite the critical conditions of these patients.

Objective: This study examines the relationship between HR and BT in children aged 0 to 18 admitted to the PICU at CHU Sainte-Justine (CHUSJ) Hospital.

Methods: Machine learning (ML) techniques, including Gradient Boosting Machines (GBM) with Quantile Regression (QR), were applied to capture the relationship between HR, BT, and age, optimizing model performance through hyperparameter tuning.

Results: Analyzing data from 4006 children, we observed a consistent trend of decreasing HR with increasing age and rising HR with higher BT ranges. Linear models often underestimated HR at lower BT ranges and overestimated it at higher ranges, especially in younger age groups. The GBM model demonstrated improved accuracy and supported a user-friendly interface for HR predictions based on BT, age, and HR percentiles. Qualitative observations indicated that linear models underestimated HR at lower BT ranges and overestimated it at higher ones, particularly in younger children. These findings challenge the direct linear association assumed in prior studies.

Conclusion: This study provides new insights into the non-linear dynamics between HR, BT, and age in critically ill children, emphasizing further research to quantify and understand these relationships.

Significance: By refining predictive models and re-evaluating traditional assumptions, this work provides valuable insights for improving clinical decision-making in PICU settings.

Objective: We designed, built, and evaluated a new sensor-instrumented clutch to expand the capabilities of quasi-passive back exos (exoskeletons and exosuits) to include force sensing, posture sensing, and versatile mode switching. Quasi-passive back exos provide workers with lifting assistance, which can reduce their back injury risk. Central to their design is a clutch mechanism that enables the exo to assist when engaged and be unobstructive when disengaged. However, current exo clutches can have limited sensing and control capabilities.

Design and methods: We designed a new clutch that integrates an encoder, solenoid, inertial measurement unit, and microprocessor to estimate exo assistance, monitor posture, and switch between engaged and disengaged modes. To validate the new capabilities, 6 participants wore a back exo during stoop and squat tasks. Data from the clutch's encoder were used to estimate assistance and trunk-thigh flexion angle, then compared to motion analysis lab measurements.

Results: The prototype estimated exo assistance with an average error of 8.8 N (0.9 Nm of lumbar torque) and trunk-thigh angle with an average error of 6.7°. This prototype also maintained the core capabilities of a quasi-passive exo by withstanding 350 N of force when the clutch was engaged, exerting 7-20 N when disengaged, and switching between clutch modes in 0.1 seconds.

Conclusion: We demonstrated an instrumented clutch that enabled exo assistance and posture monitoring, and more versatile control options, in addition to providing back relief.

Significance: This clutch increases the capabilities of quasi-passive back exos, opening new opportunities for exo research and applications.

Recently, great interests have been attracted on the online decomposition of surface electromyogram (SEMG) but current studies mainly performed validation on simulated EMG signals due to the fact that real MU activities in experimental signals were unknown. For a more comprehensive assessment of online SEMG decomposition, a two-source validation was conducted by simultaneously collecting intramuscular EMG (IEMG) and high-density SEMG signals. The IEMG signal was decomposed using a simplified version of Progressive FastICA Peel-off (PFP) method with a combination of the peel-off strategy and the valley-seeking clustering, and the decomposed motor unit (MU) spike trains were used as the ground-truth reference. For SEMG recordings, the signals within initial 5 seconds were used to offline obtain MU separation vectors and these vectors were subsequently employed to extract MU spike trains in the online stage. The matching rate of the common firing events from the ground-truth reference and online SEMG decomposition were calculated and assessed. A total of 549 and 92 MUs were identified from the SEMG and IEMG signals from 5 healthy subjects' first dorsal interosseous muscle. All the MUs decomposed from IEMG can be matched with MUs from online SEMG decomposition and the average matching rate in the online stage was (96 ± 1) %. The results highlighted the ability of separation vectors to continuously and precisely track the same MU in the experimental SEMG signals. Our study provides a more comprehensive validation perspective of online SEMG decomposition on the experimental data.

Objective: The presented work aimed to investigate neurophysiological markers of sense of presence in virtual reality. The study was based on developing and preliminary validating a neurophysiological -based approach for sense of presence evaluation.

Methods: A VR environment was designed to modulate multisensory conditions, including visual, auditory, vibrotactile stimuli. EEG, ECG, EDA signals were recorded. The Mutual Information-based sense of presence index (SoPMI) was developed as a synthetic metric for sense of presence, integrating multiple physiological signals. Signal preprocessing and analysis were conducted using EEG-based Global Field Power and Skin Conductance Level to explore their relationship with sense of presence under different VR conditions.

Results: SoPMI index demonstrated sensitivity to varying levels of multisensory integration and immersion (all p < 0.001). EEG-derived features, particularly in theta and alpha bands, were highly correlated with subjective sense of presence scores (R = 0.559, p < 0.007). Additionally, autonomic markers, such as skin conductance, showed strong associations with engagement, particularly under high-immersion conditions.

Conclusion: The study successfully identified neurophysiological markers of sense of presence and preliminarily validated the SoPMI index as a potential objective measure for VR applications. These findings establish foundation for reliable and immersive VR experiences across fields, including training, rehabilitation and industry 5.0.

Significance: By providing an objective and multimodal framework for measuring sense of presence, this research contributes to advancing VR applications where the sense of presence accurate and reliable assessment is essential. The SoPMI index offers potential for enhancing VR design and creating more effective, user-centered immersive experiences.

Objective: Microwave-induced thermoacoustic imaging (MTAI) responds to the electromagnetic properties of biological tissues, providing non-ionizing, high-resolution, and deep-penetration imaging, with significant potential for clinical diagnostics and treatment. However, the current MTAI faces issues of reduced signal-to-noise ratio (SNR) and contrast when imaging deep tissues.

Methods: In this study, we propose a passive beamforming metasurface (PB-MS) (270 mm × 270 mm × 5 mm), designed to focus microwave energy on deeper regions using phase control, enabling more sensitive MTAI of deep tissues. The PB-MS is composed of 27 superstructure units, which generate surface plasmons when excited by microwave fields. By arranging these units, the microwave field is reshaped to focus and distribute evenly, increasing the energy density in target areas. This enhances thermoacoustic signals, improving the imaging SNR and contrast.

Results: Both simulations and experiments were conducted to evaluate the practical feasibility of MTAI with PB-MS. The results showed that with PB-MS, the SNR remained as high as 22.2 dB in muscle phantoms at a depth of 7.5 cm. The MTAI system, equipped with PB-MS, is capable of detecting a minimum conductivity change of 0.095 S/m and identifying micro-liter level hemorrhages in a mouse model of hemorrhagic stroke.

Conclusion: These results demonstrate that PB-MS optimizes energy delivery in MTAI, enabling deeper and more sensitive imaging.

Significance: PB-MS effectively enhances MTAI imaging quality, representing a critical step toward its clinical application.

Objective: Selective auditory attention decoding (AAD) algorithms process brain data such as electroencephalography to decode to which of multiple competing sound sources a person attends. Example use cases are neuro-steered hearing aids or communication via brain-computer interfaces (BCI). Recently, it has been shown that it is possible to train such AAD decoders based on stimulus reconstruction in an unsupervised setting, where no ground truth is available regarding which sound source is attended. In many practical scenarios, such ground-truth labels are absent, making it, moreover, difficult to quantify the accuracy of the decoders. In this paper, we aim to develop a completely unsupervised algorithm to estimate the accuracy of correlation-based AAD algorithms during a competing talker listening task.

Methods: We use principles of digital communications by modeling the AAD decision system as a binary phase-shift keying channel with additive white gaussian noise.

Results: We show that the proposed unsupervised performance estimation technique can accurately determine the AAD accuracy in a transparent-for-the-user way, for different amounts of training and estimation data and decision window lengths. Furthermore, since different applications demand different targeted accuracies, our approach can estimate the minimal amount of training required for any given target accuracy.

Conclusion: Our proposed estimation technique accurately predicts the performance of a correlation-based AAD algorithm without access to ground-truth labels.

Significance: In neuro-steered hearing aids, the accuracy estimates provided by our approach could support time-adaptive decoding, dynamic gain control, and neurofeedback. In BCIs, it could support a robust communication paradigm with accuracy feedback for caregivers.

Objective: Modeling of Multi-Electrode Arrays used in neural stimulation can be computationally challenging since it may involve incredibly dense circuits with millions of interconnected resistors, representing current pathways in an electrolyte (resistance matrix), coupled to nonlinear circuits of the stimulating pixels themselves. Here, we present a method for accelerating the modeling of such circuits with minimal error by using a sparse plus low-rank approximation of the resistance matrix.

Methods: We prove that thresholding of the resistance matrix elements enables its sparsification with minimized error. This is accomplished with a sorting algorithm, implying efficient O (N log (N)) complexity. The eigenvalue-based low-rank compensation then helps achieve greater accuracy without significantly increasing the problem size.

Results: Utilizing these matrix techniques, we reduced the computation time of the simulation of multi-electrode arrays by about 10-fold, while maintaining an average error of less than 0.3% in the current injected from each electrode. We also show a case where acceleration reaches at least 133 times with additional error in the range of 4%, demonstrating the ability of this algorithm to perform under extreme conditions.

Conclusion: Critical improvements in the efficiency of simulations of the electric field generated by multi-electrode arrays presented here enable the computational modeling of high-fidelity neural implants with thousands of pixels, previously impossible.

Significance: Computational acceleration techniques described in this manuscript were developed for simulation of high-resolution photovoltaic retinal prostheses, but they are also immediately applicable to any circuits involving dense connections between nodes, and, with modifications, more generally to any systems involving non-sparse matrices.

Objective: Diagnosing pain in research and clinical practices still relies on self-report. This study aims to develop an automatic approach that works on resting-state raw EEG data for chronic knee pain prediction.

Method: A new feature selection algorithm called "modified Sequential Floating Forward Selection" (mSFFS) is proposed. The improved feature selection scheme can better avoid local minima andexplore alternative search routes.

Results: The feature selection obtained by mSFFS displays better class separability as indicated by the Bhattacharyya distance measures and better visualization results. It also outperforms selections generated by other benchmark methods, boosting the test accuracy to 97.5%.

Conclusion: The improved feature selection searches out a compact, effective subset of connectivity features that produces competitive performance on chronic knee pain prediction.

Significance: We have shown that an automatic approach can be employed to find a compact connectivity feature set that effectively predicts chronic knee pain from EEG. It may shed light on the research of chronic pains and lead to future clinical solutions for diagnosis and treatment.