IEEE Transactions on Biomedical Engineering最新文献

Classic ultrasound bone imaging modalities usually demand either a prior knowledge or an advanced estimation on speed of sound (SoS), which not only renders to a burdensome imaging process but also supplies a limited resolution. To overcome these drawbacks, this article proposed a frequency-domain full-waveform inversion (FDFWI) modality using phased array tomography for high-accuracy cortical bone imaging. A transmission scenario of ultrasound wave in 2-D space was presented in the frequency domain to simulate the forward wavefield propagation. Iterations in the inversion process were performed by matching the simulation wavefield to the experimental one from low to high discrete frequency points. Moreover, the association between the maximum initial frequency and the initial SoS model was explored to prevent the occurrence of cycle-skipping phenomenon, which could lead to the outcomes being trapped in local minima. The feasibility and effectiveness of the proposed imaging scheme were testified by simulation, phantom, and ex-vivo studies, with mean relative errors of cortical part being 3.18%, 8.71%, and 9.36%, respectively. It is verified that the proposed FDFWI method is an effective way for parametric imaging of cortical bone without any prior knowledge of sound speed.

Objective: Develop a multi-functional imaging system that combines 1.7μm optical coherence tomography/angiography (OCT/OCTA) to accurately interrogate Hereditary Hemorrhagic Telangiectasia (HHT) skin lesions.

Methods: The study involved imaging HHT skin lesions on five subjects including lips, hands, and chest. We assessed the attributes of both HHT lesions and the healthy vasculature around them in these individuals, employing quantifiable measures such as vascular density and diameter. Additionally, we performed scans on an HHT patient who had undergone anti-angiogenic therapy, allowing us to observe changes in vasculature before and after treatment.

Results: The results from this pilot study demonstrate the feasibility of evaluating the HHT lesion using this novel methodology and suggest the potential of OCTA to noninvasively track HHT lesions over time. The average percentage change in density between HHT patients' lesions and control was 37%. The percentage increase in vessel diameter between lesion and control vessels in HHT patients was 23.21%.

Conclusion: In this study, we demonstrated that OCTA, as a functional extension of OCT, can non-invasively scan HHT lesions in vivo. We scanned five subjects with HHT lesions in various areas (lip, ear, finger, and palm) and quantified vascular density and diameter in both the lesions and adjacent healthy tissue. This non-invasive method will permit a more comprehensive examination of HHT lesions.

Significance: This method of non-invasive imaging could offer new insights into the physiology, management, and therapeutics of HHT-associated lesion development and bleeding.

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often underutilize existing knowledge from the medical and signal processing community. In this paper, we address three shortcomings of deep learning models: motion artifact removal, degradation assessment, and physiologically plausible analysis of the PPG signal. We propose KID-PPG, a knowledge-informed deep learning model that integrates expert knowledge through adaptive linear filtering, deep probabilistic inference, and data augmentation. We evaluate KID-PPG on the PPGDalia dataset, achieving an average mean absolute error of 2.85 beats per minute, surpassing existing reproducible methods. Our results demonstrate a significant performance improvement in heart rate tracking through the incorporation of prior knowledge into deep learning models. This approach shows promise in enhancing various biomedical applications by incorporating existing expert knowledge in deep learning models.

Myocardial fibrosis, marked by excessive collagen buildup in the heart, is a crucial severity marker of heart muscle injury in several heart diseases, such as myocardial infarction, cardiomyopathies, and atrial fibrillation (AF). It is also vital for evaluating the efficacy of induced scarring (dense fibrosis) post-interventions, such as catheter ablation for AF. Cardiac MRI emerged as the gold standard for evaluating myocardial fibrosis and scarring for diagnosis and intervention planning. However, existing 3D cardiac MRI (CMR) fibrosis analysis methods are unreliable as they rely on variable thresholding and suffer from a lack of standardization and high sensitivity to typical MRI uncertainties. Importantly, these methods quantify severity based on fibrosis volume alone while ignoring the unique MRI characteristics of fibrosis distribution, which could better inform on disease severity. To address these limitations, we propose a novel thresholdfree and self-calibrating probabilistic method named "Fibrosis Signatures" for a comprehensive and reliable fibrosis analysis of 3D MRI cardiac images. Through a novel efficient (linear complexity) probabilistic encoding of 'multibillion' MRI intensity disparities into standardized probability density function, our method derives the patient's unique fibrosis signature profile and index (FSI). Our approach goes beyond mere measuring of fibrosis volume; it encodes both the extent and the unique MRI characteristics of fibrosis distribution beyond mere entropy for a more detailed evaluation of fibrosis burden/severity. Our self-calibrating design effectively adjusts for MRI uncertainties like noise, low spatial resolution, and segmentation errors to ensure robust and reproducible fibrosis evaluation pre- and post-intervention. Validated in numerical phantom and 143 in vivo MRI scans of AF patients and compared to five baseline methods, our method showed strong correlations with traditional volume measures of pre-intervention fibrosis and post-intervention scar and was up to 9- times more reliable and reproducible, highlighting its potential to enhance cardiac MRI's utility.

Objective: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used for processing this data. This study aims to reliably detect atrial fibrillation (AF) from short-term single-lead (STSL) electrocardiogram (ECG) recordings obtained in unsupervised telehealth environments.

Methods: A novel deep ensemble-based method was developed for detecting AF from STSL ECG recordings. Following this, a postprocessing algorithm was created to assess uncertainty in classified STSL ECGs and to refrain from interpretation when confidence is low. The proposed method was validated through a 5-fold cross-validation on the Cardiology Challenge 2017 (CinC2017) dataset.

Results: The deep ensemble method achieved 83.5 ± 1.5% sensitivity, 98.4 ± 0.2% specificity, and an F ₁-score of 0.847 ± 0.016in AF detection. Implementing the selective classification algorithm resulted in significant improvements, with sensitivity increasing to 92.8 ± 2.2%, specificity to 99.7 ± 0.0%, and an F ₁-score of 0.919 ± 0.016.

Conclusion: The proposed method demonstrates the feasibility of accurately detecting AF from STSL ECG recordings. The selective classification approach offers a substantial enhancement to automated ECG interpretation algorithms in telehealth solutions.

Significance: These findings highlight the potential for improving the utility of telehealth systems by integrating advanced CDSSs capable of managing uncertainty and ensuring higher accuracy, thereby improving patient outcomes in remote healthcare settings.

Objective: This study aims to identify sources of parasitic capacitance in high-density neural electrode arrays and to provide an approach for evaluating their associated capacitance values. We also represent the effect of parasitic capacitance on the electrochemical properties of electrodes.

Methods: Electrochemical impedance spectroscopy (EIS) and voltage transient (VT) measurements were employed to assess the parasitic capacitance of a 16-channel ultramicro-sized electrode array (UMEA) (8×25 μm2 electrode sites). The effect of parasitic capacitance on cyclic voltammetry (CV), EIS, and VT measurements of 20-μm diameter electrodes was assessed by comparing two different array designs: narrow and wide trace arrays.

Results: The capacitive leakage currents and charge during CV measurements were not significant, however, during current pulsing 34% underestimation of the maximum charge injection capacity corresponded to capacitive leakage. Capacitive leakage during EIS resulted in an underestimation of the electrode impedance at frequencies >1.5 kHz.

Conclusion: The electrode design and insulation thickness can play a significant role in determining the amount of capacitive leakage during current pulsing and EIS at higher frequencies.

Significance: Determining the sources and levels of capacitive leakage current in high-density neural electrode arrays, enables us to correct the measured value for the leakage current and thus estimate the electrode impedance and stimulation thresholds more accurately. This study highlights the importance of electrode design in developing high-density arrays with minimum capacitive leakage.

Objective: Accurate monitoring of fetal cardiac activity is paramount for the early detection of fetal pathologies during pregnancy. Non-invasive fetal ECG has shown promise, offering advantages over traditional fetal monitoring techniques such as cardiotocography. However, extracting fetal signals from maternal abdominal recordings poses challenges, particularly due to the presence of the vernix caseosa, a fatty layer surrounding the fetus. This study aims to investigate how vernix caseosa distribution influences ECG morphology in a novel computational framework.

Methods: A multi-compartment volume conductor, integrating fetal and maternal hearts, fetal body, amniotic fluid, and vernix caseosa embedded in a maternal torso, is constructed. Vernix caseosa distribution is varied homogeneously and heterogeneously on the fetal body. Fetal cardiac activity is simulated using a pseudo-bidomain formulation. Resulting body surface potential and ECG is analysed in terms of RDM, lnMAG, QRS complex and T-wave morphology at six abdominal sensor placements.

Results: Results indicate vernix caseosa conductive properties and presence on the fetal head do not notably interfere with ECG readings, except in rare instances where the signal strength is extremely low. Signal strength is reduced more when covering back compared to front of the fetus. Nonetheless, both scenarios have a notable impact on ECG signal and T/QRS ratio, aligning with earlier findings suggesting caution in interpreting T/QRS ratio when vernix caseosa is present.

Conclusion: The presence of vernix caseosa warrants careful consideration regarding ECG and especially T/QRS ratio interpretation.

Significance: The study contributes to advancing the understanding of non-invasive fetal ECG.

Objective: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly user experience, while the inadequacy of EEG data hinders the performance of deep learning models. To address these challenges, a task-free transfer learning strategy (TFTL) for EEG-based cross-subject & cross-dataset MI-BCI is proposed for calibration time reduction and multi-center data co-modeling.

Methods: TFTL strategy consists of data alignment, shared feature extractor, and specific classifiers, in which the label predictor for MI tasks classification, as well as domain and dataset discriminator for inter-subject variability reduction are concurrently optimized for knowledge transfer from subjects across different datasets to the target subject. Moreover, only resting data of the target subject is used for subject-specific model construction to achieve task-free.

Results: We employed three deep learning methods (ShallowConvNet, EEGNet, and TCNet-Fusion) as baseline approaches to evaluate the effectiveness of the proposed strategy on five datasets (BCIC IV Dataset 2a, Dataset 1, Physionet MI, Dreyer 2023, and OpenBMI). The results demonstrate a significant improvement with the inclusion of the TFTL strategy compared to the baseline methods, reaching a maximum enhancement of 15.67% with a statistical significance (p=2.4e-5<0.05). Moreover, task-free resulted in MI trials needed for calibration being 0 for all datasets, which significantly alleviated the calibration burden for patients before usage.

Conclusion/significance: The proposed TFTL strategy effectively addresses challenges posed by prolonged calibration periods and insufficient EEG data, thus promoting MI-BCI from laboratory to clinical application.

Objective: A robotic fast dual-arm patch clamp system with controllable mechanical stimulation is proposed in this paper for mechanosensitive excitability research of neurons in brain slice.

Methods: First, a kinematic model of a dual-arm patch clamp system combined with Monte Carlo method is developed to calculate the workspaces of recording micropipette and stimulation micropipette, and optimize the length of end effector for reducing collision incidences during operation. Then, a quantitative stimulation method to cells using one micropipette is developed based on pressing depth control. Finally, a fast robotic dual-arm patch clamp operation process is proposed based on a three-stage motion control of dual micropipettes to approach target cells and form whole-cell recording with quantitative mechanical stimulation.

Results: Experimental results on 50 pyramidal neurons in the primary visual cortex of mouse brain slices demonstrate that this system achieves a threefold throughput with a 37% improvement in the success rate of the contact process and a 42% improvement in the success rate of whole-cell recording in comparison to manual operation. With these advantages, a mechanical stimulation-regulated increase in neuron excitability is observed in primary visual cortex. The experimental results also show that the sodium ion current may be more sensitive to mechanical stimulation than potassium ion current.

Conclusion: Our system significantly improves the efficiency of mechanical stimulation induced excitability research of neurons in brain slices.

Significance: Our methods have the potential to investigate pathological and pathogenic mechanisms of mechanosensitive ion channel dysfunction-induced diseases in the future.

Objective: Current shear wave elastography methods primarily focus on 2D imaging. To explore mechanical properties of biological tissues in 3D, a four-dimensional (4D, x, y, z, t) ultrasound shear wave elastography is required. However, 4D ultrasound shear wave elastography is still challenging due to the limitation of the hardware of standard ultrasound acquisition systems. In this study, we introduce a novel method to achieve 4D shear wave elastography, named sequential-based excitation shear wave elastography (SE-SWE). This method can achieve 4D elastography implemented by a 1024-element 2D array with a standard ultrasound 256-channel system.

Methods: The SE-SWE method employs sequential excitation to generate shear waves, and utilizes a 2D array, dividing it into four sub-sections, to capture shear waves across multiple planes. This process involves sequentially exciting each sub-section to capture shear waves, followed by compounding the acquired data from these subsections.

Results: The phantom studies showed strong concordance between the shear wave speeds (SWS) measured by SE-SWE and expected values, confirming the accuracy of this method and potential to differentiate tissues by stiffness. In ex vivo chicken breast experiments, SE-SWE effectively distinguished between orientations relative to muscle fibers, highlighting its ability to capture the anisotropic properties of tissues.

Conclusion: The SE-SWE method advances shear wave elastography significantly by using a 2D array divided into four subsections and sequential excitation, achieving high-resolution volumetric imaging at 1.6mm resolution.

Significance: The SE-SWE method offers a straightforward and effective approach for 3D shear volume imaging of tissue biological properties.