IEEE Transactions on Biomedical Engineering最新文献

Objective: Panoramic basket catheters offer a rapid alternative to conventional local activation time (LAT) mapping for visualizing conduction patterns in arrhythmia patients. However, their limited spatial resolution often makes producing highly interpretable visualizations of the underlying phenomena challenging. We propose a novel interpolation method that addresses uncertainties arising from limited spatial resolution and variable tissue contact associated with basket catheters. By leveraging high sampling rates in the time domain to impose spatio-temporal constraints on the reconstruction, we enable enhanced spatial resolution in visualizing wave propagation.

Methods: We constructed overlapping triangles from adjacent electrodes. The local apparent conduction velocity (CV) was estimated from cross-correlations of signals, and the triangles were classified into linear conducting, singularity, and non-conducting types based on CV and goodness of fit. Within each triangle, a linear constraint was imposed on the reconstruction, depending on the triangle type, by projecting signals along the CV. A smoothness constraint was added to ensure consistency at clique boundaries. The hyperparameters were calibrated in an unsupervised manner.

Results: The proposed method reduced the activation time estimation error by up to 15% compared to traditional interpolation methods in simulations and showed qualitative consistency with LAT maps in clinical cases.

Conclusion: The method enhances spatial resolution in panoramic mapping, mitigating aliasing and signal artifacts, especially in regions with complex wavefronts or large inter-electrode distances.

Significance: The proposed method enables fast and accurate visualizations of panoramic conduction patterns from a single atrial cycle, potentially reducing procedure times compared to sequential mapping.

Objective: The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features.

Methods: We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation.

Results: The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models.

Conclusion: Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role.

Significance: This work provides a novel perspective on the comprehensive study of movement disorders.

Objective: This study introduces a physics-informed diffusion model (PIDM) for super-resolution (SR) reconstruction of optical coherence tomography (OCT) data.

Methods: An optimization framework was developed for maximizing the likelihood of observing an OCT image in the dataset, given the super-resolved reconstruction from a physics-informed diffusion model (PIDM) that reverses the degradations in OCT images. The image degradations were modeled as a serialization of three processes accounting for the effects of defocus, speckle noise, and digital sampling in OCT images. An analytical model for light-propagation model and a statistical model for speckle noise were derived based on the physical properties of the OCT setup. These models were then integrated with a diffusion model to reverse the degradations caused by defocus blur and digital sampling, minimizing susceptibility to noise and defocus-induced artifacts.

Results: The proposed method was employed for reconstructing images of a standard resolution target, plant tissue, and in vivo human cornea, using the complex OCT data acquired with a line-scan OCT (LS-OCT) system. The results from the PIDM exhibit improved sharpness and contrast compared to the images resulting from a few baseline methods such as standalone super-resolution using DM.

Conclusion: Complementing DM with the physics of OCT could be a viable solution for obtaining high-fidelity SR reconstruction of OCT images.

Significance: This work harnesses the power of diffusion models for super-resolution in OCT images. Such development could potentially enhance cellular-resolution OCT imaging of ophthalmic tissues, where high-fidelity images are crucial for accurate diagnosis.

Intramuscular electromyography (iEMG) decomposition identifies motor neuron (MN) discharge timings from interference iEMG recordings. When this is performed in real-time, the extracted neural information can be used for establishing human-machine interfaces. We propose a multi-channel real-time decomposition algorithm based on a Hidden Markov Model of EMG and a Bayesian filter to estimate the spike trains of motor units (MUs) and their action potentials (MUAPs). The multi-channel framework of Bayesian modelling and filtering was implemented into parallel computation using multiple GPU clusters, which ensures computational speed compatible with real-time decomposition. A decomposed-checked channel strategy is then proposed for arranging channels into groups to be processed in related GPU clusters. The algorithm was validated on six 16-channel simulated signals, three 32-channel experimental signals acquired from the human tibialis anterior muscle, and two 16-channel experimental signals acquired from the abductor digiti minimi muscle with thin-film implanted electrodes. All signals were decomposed in real time with an average decomposition accuracy 90%. In conclusion, the proposed multi-channel iEMG decomposition algorithm can be applied to implanted multi-channel electrode arrays to establish human-machine interfaces with high-information transfer.

Biophotonics includes a wide range of applications that use light-based technologies to investigate and manipulate biological systems. Traditionally, bioluminescence has been extensively used as a reporting agent in various biophotonics applications. However, its potential as a light source has not been explored. In this study, we propose the use of wireless Aequorin-based illumination as a bioinspired light-emitting source within biological tissue. This approach can have applications in a range of technologies; from optogenetics to bio-optical communications to human-brain interfaces. Drawing inspiration from the natural bioluminescent properties found in marine organisms, we designed a wireless Aequorin-based bioluminescence unit and developed an equivalent circuit model to describe the biological processes involved in illumination. Our model predicts the behavior of the bioluminescent units under various physical conditions, offering a framework for understanding how variations in physical parameters influence luminescence characteristics. In the absence of experimental studies focusing on Aequorin-based bioluminescence as a light source, our findings provide valuable guidance for researchers. These insights can help in understanding the system's behavior, designing more complex bioluminescence systems composed of multiple illumination units, and selecting parameters for future experimental research.

Objective: This paper presents a cable-driven parallel robot (CDPR) with a variable stiffness end-effector for Advanced Interventional Endoscopy.

Methods: The CDPR consists of a soft inflatable scaffold that is made from plastic laminate sheets, capable of deploying into a hollow triangular prism. The end-effector comprises multiple units linked by two cables, which also actuate the rolling joint on the end-effector tip. Variable stiffness of the end-effector is achieved by adjusting the tensions in the two cables.

Results: Through master-slave control tests, the mean manual tracking error of the robot is approximately 0.5 mm. Simulated endoscopic surgical tasks, including peg transfer, wire threading, and needle threading, demonstrate the robot's performance. Additionally, the two cables double up as force-transmission elements to estimate the contact force acting on the end-effector tip. A force estimation strategy is proposed, and preprogrammed palpation tests reveal a mean force estimation error of 0.026 N when the cable pretension is 1 N and the Bowden cable's bending angle is 90°. A study involving ten users indicates a 100% accuracy in ranking the stiffness of four blocks with visual feedback.

Conclusion: We demonstrated a variable stiffness end-effector with rolling degree of freedom and force sensing function based on a CDPR platform.

Significance: The method for achieving variable stiffness and force sensing is cost-effective and holds significant potential for application in gastrointestinal endoscopy.

The detection of Pulmonary Hypertension (PH) from the computer analysis of digitized heart sounds is a low-cost and non-invasive solution for early PH detection and screening. We present an extensive cross-domain evaluation methodology with varying animals (humans and porcine animals) and varying auscultation technologies (phonocardiography and seisomocardiography) evaluated across four methods. We introduce PH-ELM, a resource-efficient PH detection model based on the extreme learning machine that is smaller ( fewer parameters), energy efficient ( fewer watts of power), faster ( faster to train, faster at inference), and more accurate on out-of-distribution testing (improves median accuracy by 0.09 area under the ROC curve (auROC)) in comparison to a previously best performing deep network. We make four observations from our analysis: (a) digital auscultation is a promising technology for the detection of pulmonary hypertension; (b) seismocardiography (SCG) signals and phonocardiography (PCG) signals are interchangeable to train PH detectors; (c) porcine heart sounds in the training data can be used to evaluate PH from human heart sounds (the PH-ELM model preserves 88 to of the best in-distribution baseline performance); (d) predictive performance of PH detection can be mostly preserved with as few as 10 heartbeats and capturing up to approximately 200 heartbeats per subject can improve performance.

Objective: The transcranial ultrasound stimulation (TUS) on the thalamus can indirectly induce cortical response. Studies have shown that general anesthetic induced unconsciousness is related to interruption of thalamocortical connectivity. However, the neural mechanism of how anesthesia levels influence cortical responses during ultrasound thalamus stimulation has never been explored yet. And it remains unknown what cortical responses signatures are evoked by ultrasound thalamus stimulation under different anesthesia levels.

Methods: We recorded multichannel electrocorticogram (ECoG) evoked by ultrasound thalamus stimulation of rats at various isoflurane concentrations (i.e., 0.5%, 1.0%, 1.5%, and 2.0% (v/v)). We analyzed ECoG signatures in temporal, spatial, and frequency domains by using the ultrasound-evoked potentials (UEPs), omega complexity (OC), and phase amplitude coupling (PAC), respectively.

Results: The pattern of UEPs was influenced by the anesthesia level, and the response amplitude of UEPs increased with the increase in anesthesia level (0.5% vs. 1.0% and 1.5% (v/v), p<0.05). . Also, the OC of stimulated ECoG decreased with the increase in anesthesia level (at the 1.0%, 1.5% and 2.0% (v/v), p<0.05). and the modulation index of PAC was anesthesia level-dependent.

Conclusion: The cortical response induced by ultrasound thalamus stimulation is related to the anesthesia level. TUS on the thalamus combined with ECoG (TUS-ECoG) may be a potential non-invasive neuromodulation approach for understanding consciousness.

Significance: This work supplied further implications on the neuromodulatory mechanisms and evaluative applications of TUS under general anesthesia.

This study evaluates the usability and workload associated with the pneumatically attachable flexible (PAF) rail, a soft robotic device designed for safer organ manipulation and retraction during robotic-assisted partial nephrectomy (RAPN) and other laparoscopic procedures. Fourteen expert robotic and laparoscopic surgeons performed a simulated surgical retraction task using the PAF rail and standard surgical instruments. Usability was assessed using the System Usability Scale (SUS), and workload was measured with the NASA-TLX. Qualitative feedback was also collected to explore surgeon perceptions, and analysed thematically. Histopathological analysis was conducted to assess tissue integrity following instrument interaction. The PAF rail achieved SUS scores exceeding the good usability threshold, particularly among urology surgeons. However, its use was associated with increased cognitive load and longer task completion times, especially for less experienced surgeons. Histopathological analysis showed no additional tissue damage from the PAF rail compared to existing instruments. This study demonstrates that the PAF rail has potential as a safe and effective method for organ manipulation and retraction, achieving good usability and showing no additional tissue damage compared to existing instruments.

Objective: The understanding of the mechanisms driving vascular development is still limited. Techniques to generate vascular trees synthetically have been developed to tackle this problem. However, most algorithms are limited to single trees inside convex perfusion volumes. We introduce a new framework for generating multiple trees inside general nonconvex perfusion volumes.

Methods: Our framework combines topology optimization and global geometry optimization into a single algorithmic approach. Our first contribution is defining a baseline problem based on Murray's original formulation, which accommodates efficient solution algorithms. The problem of finding the global minimum is cast into a nonlinear optimization problem (NLP) with merely super-linear solution effort. Our second contribution extends the NLP to constrain multiple vascular trees inside any nonconvex boundary while avoiding intersections. We test our framework against a benchmark of an anatomic region of brain tissue and a vasculature of the human liver.

Results: In all cases, the total tree energy is improved significantly compared to local approaches.

Conclusion: By avoiding intersections globally, we can reproduce key physiological features such as parallel running inflow vessels and tortuous vessels.

Significance: The ability to generate non-intersecting vascular trees inside nonconvex organs can improve the functional assessment of organs.