IEEE Transactions on Biomedical Engineering最新文献

This study evaluates a fused-data transrectal electrical impedance tomography (TREIT) method for prostate cancer imaging on a set of 22 ex vivo prostates. A previously optimized TREIT algorithm is utilized, and novel validation and fusion approaches leveraging pathology information are considered. Overall, the aim was to increase the sensed volume of a standard 12-core prostate biopsy by adding TREIT imaging. Two TREIT approaches were considered: 1. including prostate boundary information (EIT-P) and 2. including prostate and tumor boundary information (EIT-P+T). Both simple electrical impedance spectroscopy (EIS) metrics and the two imaging approaches (EIT-P and EIT-P+T) were evaluated with respect to biopsy core, 3D (EIT-P) image, and tumor-grade data. Best AUCs of 0.85, 0.84, and 0.83 were found when considering increasing volumes of tissue (0.8%, 2.7%, and 15% of the prostate). The largest measurement volume (15%), which utilized EIT-P, sensed significantly more prostate tissue than the standard biopsy only approach (<1%). These represent large improvement compared to prior clinical EIS biopsy and TREIT studies. Tumor-grade analysis (via EIT-P+T) appears to show promise but more data is required to confirm this. Overall, the study made important strides in developing the TREIT technique and further investigation, likely in an in vivo study, appears merited.

Tumor Treating Fields (TTFields) utilize alternating fields (AC fields) within 100-300 kHz and electric field strengths above 1 V/cm for glioblastoma (GBM) treatment. However, the electric field is often reduced to a relatively low value (below 1 V/cm) due to the unavoidable thermal effects induced by Joule heating on the patient's skin.

Objective: This study proposes a pulsed TTFields to enhance therapy effect, while reducing thermal effects.

Methods: This work designed a TTFields generator to output 200 kHz AC fields. Cell experiments were conducted to compare cell viability between pulsed and conventional TTFields. A gel platform was used to measure temperature rises under clinical parameters of TTFields. A realistic head model with a tumor was simulated to analyze electric field and thermal distributions.

Results: The designed generator can output two separate TTFields signals with 100 V voltage amplitude and 2000 mA current amplitude, meeting clinical trial requirements. Pulsed TTFields (10% duty cycle, 3.37 V/cm) achieved significantly lower cell viability (53.07%) than continuous TTFields (1.07 V/cm, 84.76%) while maintaining similar temperature rises. Gel experiments confirmed comparable temperature rises for both protocols. Simulations on a realistic head model demonstrated that pulsed TTFields achieved broader tumor coverage (electric field >1 V/cm) compared to continuous TTFields under equivalent thermal conditions.

Conclusion: Pulsed TTFields can generate higher electric fields in targeted regions, significantly inhibiting cell proliferation while reducing thermal risks compared to continuous TTFields.

Significance: The proposed pulsed TTFields may provide an optimized treatment method to enhance GBM therapy efficacy.

Clinical diagnosis of disorders of conscious ness (DOC) suffers from a high misdiagnosis rate, particularly in differentiating the minimally conscious state (MCS) from the vegetative state/unresponsive wakefulness syndrome (VS/UWS). Recent studies have linked pain perception to the level of consciousness. This study proposes a dynamic local-global spatiotemporal transformer (DLGSTT) network for estimating pain intensity from facial expressions. The DLGSTT network integrates a global multi-scale feature extraction module with a local attention feature ex traction module to efficiently capture diverse features in facial expressions and enhance the perception of expression changes. Additionally, a discrete cosine transform (DCT) enhanced temporal transformer module is incorporated to extract temporal features from the dynamic changes in facial expressions, with pain intensity scores used to quantify pain perception. Experimental results demonstrate that the DLGSTT network outperforms state-of-the-art algorithms on public datasets. Furthermore, when applied to a self-collected dataset of 33 DOC patients, the results show a significant correlation between pain intensity and levels of consciousness, and reveal gender-based differences in pain perception thresholds. Our method is validated as a feasible clinical tool for the auxiliary diagnosis of DOC patients, serving as a valuable complement to behavioral scales and potentially improving diagnostic accuracy.

Objective: Oscillometric finger pressing is a potential method for smartphone-based blood pressure (BP) monitoring. A photoplethysmography (PPG)-force sensor unit measures the slowly increasing finger pressure applied by the user under visual guidance and the resulting variable blood volume oscillations ("AC PPG"). BP can then be estimated from the oscillation height versus finger pressure function. The non-oscillating component of PPG ("DC PPG") during oscillometric finger pressing was investigated.

Methods: The total (AC+DC) PPG waveform, finger pressure, and ECG waveform during finger pressing were measured with a modified custom system along with arm cuff BP in volunteers. A mathematical model accounting for the arterial compliance curve and tissue compression was developed to explain the measured total PPG waveform versus finger pressure function. The model predicted that DC PPG (average of the total PPG over each heartbeat) versus finger pressure function will show a fiducial marker (bend) near finger systolic BP (SP). An algorithm was developed to detect this bend and estimate arm SP from the finger pressing measurements.

Results: The model explained the measured total PPG waveform versus finger pressure function over the finger pressure range that is relevant to BP estimation. The model-based algorithm yielded a correlation coefficient of 0.90 and a precision error of 9.2 mmHg against cuff SP (N = 18).

Conclusion: An easy-to-understand model can explain the total PPG waveform during finger pressing, and SP can be estimated from the DC (non-oscillating) PPG versus finger pressure function.

Significance: These findings may prove useful in converting ubiquitous smartphones into BP sensors.

 Objective: To develop, and test, a fast image 3D reconstruction method for partial boundary data electrical impedance tomographic absolute and time-difference imaging.

Methods: Two complex geometrical optics based methods are presented: Calerón's method which employs a linear Fourier transform, and the t^exp method which makes use of a tailor-made nonlinear Fourier transform. The methods are tested on simulated and experimental data, and their reconstructions compared to reference reconstructions from standard linear difference imaging and total variation regularization.

Results: The proposed methods provide good localization of targets, for both absolute and time-difference imaging, when large portions of the domain are inaccessible for measurement, e.g., stroke monitoring.

Conclusion: The proposed algorithms require no iteration and provide informative absolute or time-difference images exceptionally quickly in under 2 seconds for complicated domain shapes. The algorithms perform well under high levels of noise and incorrect domain modeling.

Significance: As most medical applications of electrical impedance tomography are limited to partial boundary data, the development of partial boundary algorithms is highly desirable. While iterative schemes have been used traditionally, their high computational cost can make them cost-prohibitive for applications that need fast imaging.

Objective: Healthy individuals have the ability to overcome perturbations when walking without falling. They have multiple stability strategies at their disposal, but it remains unclear how these different strategies compensate for one another when one may be limited due to external factors. The objective of the current study was to determine how the different stability strategies compensate for one another when mediolateral perturbations were applied.

Methods: We performed both human experiments and computational modelling. The human experiments involved imposed restrictions on the different stability strategies while perturbations were applied and measuring the response of the other strategies. The stepping strategy was limited using visual feedback of step width projected onto the ground, the ankle strategy was restricted using narrow strips of rubber under the foot, and the trunk strategy was restricted using a brace. Similarly, in the computational model, we observed changes in the remaining strategies once one of the balance strategies was removed.

Results: In our gait study, we found that the limitation of one strategy did not result in compensatory behaviour in the remaining strategies. However, our computational model did exhibit compensatory behaviour when strategies were removed.

Conclusion: These conflicting results suggest that compensatory behaviour has the potential to be beneficial for overcoming perturbations but is not utilized by healthy individuals.

Significance: The mismatch of experimental and modelling results suggests that human responses are not purely motivated by the continuation of forward motion but instead by a combination of objectives.

Event-related potential (ERP)-based brain-computer interface (BCI) systems are approaching sub-microvolt-level resolution, enabling detailed decoding of sophisticated cognitive processes. This progress has increased the demand for robust classifiers. Current algorithms encounter two fundamental challenges when decoding ERPs: data scarcity and class imbalance. To address these challenges, we propose a joint-shrinkage pattern matching (JSPM) algorithm consisting of two modules. First, a novel joint-shrinkage spatial filter is constructed by integrating shrinkage-based regularization with the ℓℓ22,pp norm. This regularization approach effectively bridges the gap between complex structured regularization and implementation simplicity, which introduces automated regularization to enhance module robustness under data-scarce conditions. The ℓℓ22,pp-norm provides a flexible feature distance measurement, enabling adaptation to data quality variability. Second, a weighted template matching module mitigates decision boundary shift caused by class imbalance. Using error-related potentials (ErrPs) as representative signals, we validated the algorithm through comprehensive comparisons. JSPM significantly outperformed 14 state-of-the-art classifiers on one self-collected and two public ErrP datasets. With only 40 imbalanced training samples, it achieved up to 14.84% higher average balanced accuracy (bAcc) than competing methods, maintaining a 4.88% average bAcc advantage over its nearest competitor. Notably, JSPM significantly enhanced inter-class discriminability for ErrP features with approximately 1 μV amplitude, achieving a maximum bAcc enhancement of 8.80%compared to deep learning methods. Overall, JSPM effectively addresses small-sample and imbalanced ERP decoding in BCI systems, facilitating the transition from laboratory research to real-world applications.

Objective: A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. This paper tackles the problem of decoding EEG signals across different headsets, which is challenging due to differences in the number and locations of the electrodes.

Methods: We propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains.

Results: Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms.

Significance: Our approach enables effective transfer between heterogenous EEG headsets, improving and expediting BCI calibration.

The superior image quality and excellent contrast offered by Magnetic Resonance Imaging (MRI) make it an ideal tool for guiding interventional procedures, particularly in targeting tumors within soft tissues. This paper presents an innovative robotic system, tailored for Magnetic Resonance (MR) environments, leveraging the benefits of MRI's high-resolution imaging capabilities for precise tumor targeting. The robot, designed with four degrees of freedom (DOFs), is driven by four novel Harmonic Pneumatic Motors, ensuring Magnetic Resonance (MR) compatibility. This motor utilizes a harmonic gearbox mechanism, incorporating a pair of flex and circular splines, with torque generated through the deformation of the flex spline. It achieves a step size of 0.9° and delivers a maximum measured output torque of 825 mN·m. The robot's design includes a dual-stage needle guide, each stage is supported by two arms, enhancing its targeting accuracy. Experimental tests have demonstrated the robot's high positioning accuracy of 1.56 mm. Furthermore, MR testing confirms that the robot's presence results in a negligible signal-to-noise ratio (SNR) reduction, well within acceptable limits. The introduction of the Harmonic Pneumatic Motor-Driven Robot presents an improvement of compact MRI robots in the field, providing a potent combination of precision, safety, and compatibility in MRI-guided interventional procedures.