IEEE Transactions on Biomedical Engineering最新文献

Over the past decade, ultrasound microvasculature imaging has seen the rise of highly sensitive techniques, such as ultrafast power Doppler (UPD) and ultrasound localization microscopy (ULM). The cornerstone of these techniques is the acquisition of a large number of frames based on unfocused wave transmission, enabling the use of singular value decomposition (SVD) as a powerful clutter filter to separate microvessels from surrounding tissue. Unfortunately, SVD is computationally expensive, hampering its use in real-time UPD imaging and weighing down the ULM processing chain, with evident impact in a clinical context. To solve this problem, we propose a new approach to implement SVD filtering, based on simplified and elementary operations that can be optimally parallelized on GPU (GPU sSVD), unlike standard SVD algorithms that are mainly serial. First, we show that GPU sSVD filters UPD and ULM data with high computational efficiency compared to standard SVD implementations, and without losing image quality. Second, we demonstrate that the proposed method is suitable for real-time operation. GPU sSVD was embedded in a research scanner, along with the spatial similarity matrix (SSM), a well-known efficient approach to automate the selection of SVD blood components. High real-time throughput of GPU sSVD is demonstrated when using large packets of frames, with and without SSM. For example, more than 15000 frames/s were filtered with 512 packet size on a 128 × 64 samples beamforming grid. Finally, GPU sSVD was used to perform, for the first time, UPD imaging with real-time and adaptive SVD filtering on healthy volunteers.

Objective: The objective of this study is to propose a novel methodology for intracranial pressure (ICP) waveform subpeak identification by incorporating arterial blood pressure (ABP) and electrocardiogram (ECG) signals from patients who have undergone traumatic brain injury (TBI).

Methods: This approach consisted of 1) multimodal signal pre-processing and initial manual ICP waveform morphology labeling, 2) semi-supervised training of a support vector machine (SVM) ICP waveform morphological classifier, and 3) a dynamic time warping barycenter averaging (DBA) based ICP waveform template generation and derivative dynamic time warping (DDTW)-driven ICP waveform subpeak mapping from template to incoming processed waveforms.

Results: This proposed framework was evaluated on 30,000 ICP waveforms and resulted in an overall subpeak identification accuracy score of 98.2%.

Conclusion: The results showcased an improvement over existing methodologies and showed resilience to variations in ICP waveform morphologies from patient to patient due to the incorporation of a subject matter expert (SME) to accommodate new and unseen ICP morphologies.

Significance: The robustness of this comprehensive approach enabled the analysis of ICP morphological features over time to provide clinicians with crucial insights regarding the development of secondary pathologies in patients and facilitate monitoring their physiological state.

For people with Type 1 diabetes (T1D), accurate blood glucose (BG) forecasting is crucial for the effective delivery of insulin by Artificial Pancreas (AP) systems. Deep learning frameworks like Long Short-Term-Memory (LSTM) have been widely used to predict BG using continuous glucose monitor (CGM) data. However, these methods usually require large amounts of training data for personalized forecasts. Moreover, individuals with diabetes exhibit diverse glucose variability (GV), resulting in varying forecast accuracy. To address these limitations, we propose a novel deep learning framework: Incrementally Retrained Stacked LSTM (IS-LSTM). This approach gradually adapts to individuals' data and employs parameter-transfer for efficiency. We compare our method to three benchmarks using two CGM datasets from individuals with T1D: OpenAPS and Replace-BG. On both datasets, our approach significantly reduces root mean square error compared to the state of the art (Stacked LSTM): from 14.55 to 10.23mg/dL (OpenAPS) and 17.15 to 13.41mg/dL (Replace-BG) at 30-minute Prediction Horizon (PH). Clarke error grid analysis demonstrates clinical feasibility with at least 98.81% and 97.25% of predictions within the clinically safe zone at 30- and 60-minute PHs. Further, we demonstrate the effectiveness of our method in cold-start scenarios, which helps new CGM users obtain accurate predictions.

Objective: Accurate alignment of long bone fractures under minimally invasive procedures is a prerequisite for excellent treatment outcomes. However, the existing technologies suffer from the drawbacks of complex operations and excessive dependence on the surgeon's expertise. To solve these problems, we have developed a novel computer-assisted system to achieve rapid and effective reduction of fractures.

Methods: The automatic registration of the bone-fixator is accomplished based on the principal component analysis and the markers recognition. Then, the fracture reduction target is acquired by utilizing the Iterative Closest Point algorithm on the mirrored contralateral bone model. Next, the optimal reduction trajectory is automatically generated by considering collision detection, muscle pull force analysis, and trajectory optimization. Finally, the strut adjustment plan of the fixator is provided to the surgeon, combined with the results of bone-fixator registration.

Result: Modeling experiments verified the high accuracy of the system registration and the superiority of the reduction planning method, and clinical trials demonstrated the effectiveness and feasibility of the proposed system for fracture treatment.

Conclusion: The proposed system facilitates accurate and efficient planning of fracture reduction for surgeons through simple manipulation.

Significance: Our system enables a one-stop automatic acquisition of prescriptions for external fixation treatment of fractures.

Objective: The isolated mammalian retina may serve as a sensitive biosensor for preclinical drug testing, including eye drugs and a broader range of pharmaceuticals. To facilitate testing with minimal amounts of drug molecules or nanostructures, we developed a closed-perfusion transretinal electroretinography (tERG) setup.

Methods: The major challenge with small amounts of circulating perfusate was maintaining retinal viability and stability during long experiments. We conducted ex vivo tERG using WT C57BL/6J and Gnat1^-/- mice to assess rod- and cone-mediated light signals. The dark-adapted retina was stimulated with full-field light flashes while perfused at 5-6 ml/min.

Results: The minimum perfusate needed in our closed-circulation was around 50 ml. Penicillin-Streptomycin (Pen-Strep) was indispensable for long recordings. Rod responses remained stable for at least 42 hours, the longest recording we conducted, with the retina still responsive, and rod and cone bipolar cell responses for up to 12 hours. IBMX (3-isobutyl-1-methylxanthine), a non-specific phosphodiesterase (PDE) inhibitor with reversible effects, validated our setup. We used our setup to test the zwitterionic polymer poly(sulfobetaine methacrylate) (PSMBA), serving as a promising material for thermoresponsive nanostructures, and the corresponding monomer SBMA for possible harmful effects on mouse rod and bipolar cell functioning.

Conclusion: Our closed-perfusion tERG setup enables long experiments with small amounts of perfusate. PSMBA or SBMA had no effect on rod and bipolar cell responses.

Significance: This method is applicable for assessing drug functionality, as well as conducting preliminary biocompatibility and toxicity testing using small amounts of molecules or nanostructures that could impact neuronal or synaptic function.

Objective: Event-related potentials (ERPs) reflect electropotential changes within specific cortical regions in response to specific events or stimuli during cognitive processes. The P300 speller is an important application of ERP-based brain-computer interfaces (BCIs), offering potential assistance to individuals with severe motor disabilities by decoding their electroencephalography (EEG) to communicate.

Methods: This study introduced a novel speller paradigm using a dynamically growing bubble (GB) visualization as the stimulus, departing from the conventional flash stimulus (TF). Additionally, we proposed a "Lock a Target by Two Flashes" (LT2F) method to offer more versatile stimulus flash rules, complementing the row and column (RC) and single character (SC) modes. We applied the "Sub and Global" multi-window mode to EEGNet (mwEEGNet) to enhance classification and explored the performance of eight other representative algorithms.

Results: Twenty healthy volunteers participated in the experiments. Our analysis revealed that our proposed pattern elicited more pronounced negative peaks in the parietal and occipital brain regions between 200 ms and 230 ms post-stimulus onset compared with the TF pattern. Compared to the TF pattern, the GB pattern yielded a 2.00% increase in online character accuracy (ACC) and a 5.39 bits/min improvement in information transfer rate (ITR) when using mwEEGNet. Furthermore, results demonstrated that mwEEGNet outperformed other methods in classification performance.

Conclusion and significance: These results underscore the significance of our work in advancing ERP-based BCIs.

Combination therapy with motor imagery (MI)-based brain-computer interface (BCI) and repetitive transcranial magnetic stimulation (rTMS) is a promising therapy for poststroke neurorehabilitation. However, with patients' individual differences, the clinical effects vary greatly. This study aims to explore the hypothesis that stroke patients show individualized cortical response to rTMS treatments, which determine the effectiveness of rTMS-induced MI decoding enhancement. We applied four kinds of rTMS treatments respectively to four groups of subacute stroke patients, twenty-six patients in total, and observed their EEG dynamics, MI decoding performance, and Fugl-Meyer assessment changes following 2-week neuromodulation. Four treatments consisted of ipsilesional 10 Hz rTMS, contralesional 1 Hz rTMS, ipsilesional 1 Hz rTMS, and sham stimulation. Results showed stroke patients with different neural reorganization patterns responded individually to rTMS therapy. Patients with cortical lesions mostly showed contralesional recruitment and patients without cortical lesions mostly presented ipsilesional focusing. Significant activation increases in the ipsilesional hemisphere (pre: -15.7% ∓ 8.2%, post: -17.3% ∓ 8.1%, p = 0.037) and MI decoding accuracy enhancement (pre: 76.3 ± 13.8%, post: 86.6 ± 8.2%, p = 0.037) were concurrently found in no-cortical-lesion patients with ipsilesional activation treatment. In the group of patients without cortical lesions, recovery rate in those receiving ipsilesional activation therapy (23.5 ± 10.4%) was higher than those receiving ipsilesional suppression therapy (9.9 ± 9.3%) (p = 0.041). This study reveals that tailoring neuromodulation therapy by recognizing cortical activation patterns is promising for improving effectiveness of the combination therapy with BCI and rTMS.