IEEE Transactions on Biomedical Engineering最新文献

This paper introduces a wirelessly powered multimodal animal physiological monitoring application-specific integrated circuit (ASIC). Fabricated in the 180 nm process, the ASIC can be integrated into an injectable device to monitor subcutaneous body temperature, electrocardiography (ECG), and photoplethysmography (PPG). To minimize the device size, the ASIC employs a miniature receiver (Rx) coil for wireless power receiving and data communication through a single inductive link operating at 13.56 MHz. We propose a folded L-shape Rx coil with improved coupling to the transmitter (Tx) coil, even in the presence of misalignment, and enhanced quality factor. The ASIC functions alternatively between recording and sleeping modes, consuming 2.55 μW on average. For PPG measurements, a reflection-type PPG sensor illuminates an LED with tunable current pulses. A current-input analog frontend (AFE) amplifies the current of a photodiode (PD) with 30.8 pARMS current input-referred noise (IRN). The ECG AFE captures ECG signals with a configurable gain of 45-80 dB. The temperature AFE achieves 0.02 ̊C inaccuracy within a sensing range between 27-47 ̊C. The AFE outputs are sequentially digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with an effective number of bits (ENOB) of 8.79. To improve the reliability of data transmission, we propose a memory-assisted backscatter scheme that stores ADC data in an off-chip memory and transmits it when the coupling condition is stable. This scheme achieves a package loss rate (PLR) lower than 0.2% while allowing 24-hour data storage. The device's functionality has been evaluated by in vivo experiments.

Objective: Animal models of drug-resistant epilepsy represent an important resource for discovering new drug targets and testing experimental medicines. Intra-amygdala microinjection of kainic acid in mice is one of the most widely regarded models of drug-resistant epilepsy. Mice develop acute status epilepticus, which diminishes after a few hours and then, within a few days, mice display spontaneous seizures (epilepsy). The frequency of spontaneous seizures varies between mice, with some developing low or high seizure rates. The ability to predict soon after status epilepticus, which mice will go on to develop a normal frequency of seizures, would enable a significant reduction in resources and EEG reviewing time and lead to humane early end-points for the mice with low or high seizure rates.

Method: In this study, we developed two machine learning models, a feature-based and transfer learning-based approach, for predicting the emergent spontaneous seizure rates in the intra-amygdala kainic acid model based on the acute EEGs recorded in mice during status epilepticus lasting 40 minutes. The method was trained on data from 28 mice and subsequently tested on data from 16 mice.

Results: The feature-based and transfer learning-based models achieved accuracies of 69% and 75%, respectively on the test set in classifying emergent epilepsy as normal or outlier (i.e. low-frequency or high-frequency seizure rate).

Conclusion: A limitation of the intra-amygdala kainic acid model has been the loss of time and resources from generating mice with low or high rates of spontaneous seizures. To date, no other research has attempted to predict emergent spontaneous seizure rates. The feature-based and transfer learning-based models will assist researchers in identifying mice with a normal frequency of seizures before the onset of spontaneous seizures.

Significance: We have implemented this approach as a web server, which can potentially reduce the time and resources spent analysing the EEGs of mice who develop low-frequency or high-frequency seizure rates. This will enable the early humane endpoint of outlier mice, which aligns with the principles of the responsible use of animals in research and simultaneously speeds up preclinical anti-epilepsy drug discovery.

Objective: To develop a novel Magnetic Resonance Imaging (MRI)-guided Near-Infrared Spectroscopic Tomography (MRg-NIRST) imaging system with an MRI-compatible breast optical interface for breast imaging.

Objective: To propose a 3D nonrigid registration method that accurately estimates the 3D displacement field from artifact-corrupted Coronary Magnetic Resonance Angiography (CMRA) images.

Methods: We developed a novel registration framework for registration of artifact-corrupted images based on a 3D U-Net initializer and a deep unrolling network. By leveraging a supervised learning framework with training labels estimated from fully-sampled images, the unrolling network learns a task-specific motion prior which reduces motion estimation biases caused by undersampling artifacts in the source images. We evaluated the proposed method, UNROLL, against an iterative Free-Form Deformation (FFD) registration method and a recently proposed Respiratory Motion Estimation network (RespME-net) for 6-fold (in-distribution) and 11-fold (out-of-distribution) accelerated CMRA.

Results: Compared to the baseline methods, UNROLL improved both the accuracy of motion estimation and the quality of motion-compensated CMRA reconstruction at 6-fold acceleration. Furthermore, even at 11-fold acceleration, which was not included during training, UNROLL still generated more accurate displacement fields than the baseline methods. The computational time of UNROLL for the whole 3D volume was only 2 seconds.

Conclusion: By incorporating a learned respiratory motion prior, the proposed method achieves highly accurate motion estimation despite the large acceleration.

Significance: This work introduces a fast and accurate method to estimate the displacement field from low-quality source images. It has the potential to significantly improve the quality of motion-compensated reconstruction for highly accelerated 3D CMRA.

Polysomnography (PSG) is the gold standard for sleep staging in clinics, but its skin-contact nature makes it uncomfortable and inconvenient to use for long-term sleep monitoring. As a complementary part of PSG, the video cameras are not utilized to their full potential, only for manual check of simple sleep events, thereby ignoring the potential for physiological and semantic measurement. This leads to a pivotal research question: Can camera be used for sleep staging, and to what extent? We developed a camera-based contactless sleep staging system in the Institute of Respiratory Diseases and created a clinical video dataset of 20 adults. The camera-based feature set, derived from both physiological signals (pulse and breath) and motions all measured from a video, was evaluated for 4-class sleep staging (Wake-REM-Light-Deep). Three optimization strategies were proposed to enhance the sleep staging accuracy: using motion metrics to prune measurement outliers, creating a more personalized model based on the baseline calibration of waking-stage physiological signals, and deriving a specialized feature for REM detection. It achieved the best accuracy of 73.1% (kappa = 0.62, F1-score = 0.75) in the benchmark of five sleep-staging classifiers. Notably, the system exhibited high accuracy in predicting the overall sleep structure and subtle changes between different sleep stages. The study demonstrates that camera-based contactless sleep staging is a new value stream for sleep medicine, which also provides clinical and technical insights for future optimization and implementation.

Objective: Endovascular surgery requires accurate measurement of parameters such as pressure, temperature, and biomarkers within vessels for real-time tissue response monitoring and ensuring targeted therapeutic interventions. However, the availability of small tip-based sensors capable of precise application, for example, navigating an aneurysm's lumen, is limited. With their capabilities for real-time analysis, flexibility, and biocompatibility, optical fiber sensors (OFS) hold promise in addressing this need. This proof-of-concept study investigates the feasibility of OFS in endovascular surgery scenarios.

Methods: The sensor is based on a single-mode silica fiber with an interferometric forward-facing thin-film tip. The thin-film materials may be tailored for detecting various physical parameters and, when functionalized, also specific analytes. Materials applied in this sensor are thin metal oxides deposited using magnetron sputtering. A full-scale 3D-printed vascular model was employed to simulate endovascular setup.

Results: The experiments showed the high mechanical robustness of the approach, i.e., the sensor maintained functionality while being maneuvered through the endovascular model. The forward-facing tip remained intact and worked adequately, ensuring consistent and stable readouts. Moreover, the fiber showed sufficient flexibility, with no significant bending loss observed during simulations. Finally, the performance of the OFS in bovine serum samples was assessed. The sensor performed well in serum, and the results suggest that low-concentration serum may be used to reduce nonspecific surface interactions.

Conclusion: Overall, this OFS system offers a promising solution for endovascular surgery and other biomedical applications, allowing for precise and on-the-spot analysis.

Significance: Our study pioneers the feasibility of thin-film interferometric label-free OFS with a forward-facing sensitive area for sensing during endovascular procedures.

Aortic dissection leads to late complications due to chronic degeneration and dilatation of the false lumen. However, the interaction between hemodynamics and microstructural remodeling driving long-term changes is not fully understood. This study examines the progression of a patient's aortic dissection, tracked from pre-dissection to the chronic phase using computed tomography angiography. Fluid-structure interaction models that account for tissue prestress, external support, and anisotropic properties were used to analyze hemodynamic markers. Each aortic wall layer had distinct thicknesses and material properties. The boundary conditions were guided by in vitro 4D-flow MRI and the patient's blood pressure. Quantitative measurements during routine clinical care showed that aortic dilatation was most significant distal to the left subclavian artery, reaching 6cm in the chronic phase. Simulations resulted in a flow jet velocity through the entry tear that peaked at 185cm/s in the subacute phase and decreased to 123 to 133 cm/s in the chronic phase, corresponding to an increased entry tear size. Flow jet impingement on the false lumen resulted in a localized pressure increase of 11 and 2mmHg in the subacute and chronic phases, with the wall shear stress reaching 4,Pa. These hemodynamic changes appear to be the main drivers of aortic growth and morphological changes. Despite moderate overall flap movement, in-plane displacement increased from 0.6 to 1.8mm as disease progressed, which was associated with an overall increase in aortic diameter. Additional simulations with a significant reduction in flap stiffness during the subacute phase resulted in increased flap motion up to 9.5mm. Although these results are based on a single patient, they suggest a strong relationship between hemodynamics and aortic growth.

Introduction: Adjustable magnetic fields are essential for precisely steering drug-loaded magnetic nanoparticles in cancer therapy. Since electromagnets require high currents to achieve a strong magnetic force, this paper presents a new approach combining electromagnets and permanent magnets.

Objective: The basic idea of the hybrid array is to use the strong and low-cost magnetic field of permanent magnets and superimpose them with the field of electromagnets via a Halbach arrangement. This results in a constructive and destructive superposition of the magnetic field, which can easily be reversed by the applied current's direction. Moreover, the current's magnitude can be reduced dramatically to 2 A, as the primary magnetic flux comes from the permanent magnets.

Methods: To the authors' knowledge, this is the first paper proposing a magnetic hybrid array for steering magnetic nanoparticles in a velocity flow. The array was validated in simulations using COMSOL Multiphysics and measurements in a tube flow system. In contrast to state-of-the-art publications, the particle distribution was determined quantitatively.

Results: In this proof of concept, the simulation and measurement results fit well. It was demonstrated that the magnetic force is adjustable via the current and that the magnetic field of permanent magnets can be eliminated by superimposing the field of electromagnets, also indicated by the particle distribution. Furthermore, gravitation has a significant influence on particle distribution.

Significance: The proposed system combines the advantages of permanent magnets and electromagnets. Hence, the induced heat that damages tissue is decreased, which is crucial for bringing the setup into clinical treatments.

Optical coherence tomography (OCT) is being widely applied in clinical studies to investigate insight into the retina under the retinal pigment epithelium. Optical coherence tomography angiography (OCTA) is one of the functional extensions of OCT, for visualizing retinal circulation. Due to obstruction of light propagation, such as vitreous floaters or pupil boundaries, OCTA remains challenged by shadow artifacts that can disrupt volumetric data. Detecting and removing these shadow artifacts are crucial when quantifying indicators of retinal disease progression. We simplified an optical attenuation model of shadow formation in OCTA to a linear illumination transformation. And learn its parameters using an adversarial neural network. Our framework also consists of a sub-network for shadows automatic detection. We experimented our method on 28 OCTA images of normal eyes and compared the non-perfusion area (NPA), an index to measure retinal vascularity. The results showed that the NPA adjusted to a reasonable range after image processing using our method. Furthermore, we tested 150 OCTA images of synthesis artifacts, and the mean absolute error(MAE) values reached 0.83 after shadow removal.

Objective: Myoelectric control technology has important application value in rehabilitation medicine, prosthesis control, human-computer interaction (HCI) and other fields. However, the user dependence of electromyography (EMG) pattern recognition is one of the key problems hindering the implementation of robust myoelectric control applications. Aimed at solving the user dependence problem, this paper proposed a novel instruction gesture set determination scheme for EMG pattern recognition in user-independent mode.

Methods: The scheme uses T-distributed stochastic neighbor embedding (T-SNE) dimensionality reduction to analyze high-dimensional surface EMG data from multiple users and gestures. This process can identify gesture combinations with minimal individual differences and high separability.

Results: The proposed scheme was validated using two large-scale EMG gesture databases with different acquisition devices, subjects, and gestures. Optimal and inferior gesture sets of varying sizes were identified. In recognition experiments conducted in both user-independent and electrode-offset modes, the optimal gesture sets demonstrated significantly higher recognition accuracies compared to the inferior sets, with improvements ranging from 12.57% to 36.92%.

Conclusion: The results demonstrated that the separability of the obtained optimal gesture sets was significantly superior to that of the inferior sets, confirming the effectiveness of the proposed scheme in reducing user dependence in EMG pattern recognition.

Significance: The study has certain application value to promote the development of myoelectric control technology. Specifically, the scheme proposed can be used to determine instruction gesture sets with low user dependence and high separability for myoelectric control applications.