IEEE Transactions on Biomedical Engineering最新文献

Objective: Identifying the first (S1) and second (S2) heart sounds from phonocardiogram (PCG) signals is an essential step in automating the diagnosis of cardiac conditions such as irregular heartbeat, valve misfunctions, and heart failure. Recent research inspired by image segmentation has shown promise in utilising deep neural networks for point-wise PCG segmentation with the support of synchronised electrocardiograms (ECG). This paper shifts the focus from point-wise segmentation to identifying the onset/offset of S1 and S2 in the PCG signal.

Methods: We incorporate the ECG signal and its keypoints to improve the detection of the heart sounds. Our proposed method employs a joint classifier-regressor architecture for predicting the probability and the location of onset/offset in the PCG.

Results: When evaluated on the largest publicly available PhysioNet/CinC 2016 dataset, the proposed approach outperforms existing state-of-the-art methods, achieving a sensitivity of 0.97 and a positive predictive value of 0.98 in identifying midpoints of S1 and S2 segments. It also identifies the onset/offset locations with an 11.11 ms error.

Conclusion: It is evident that identifying the transitions simplifies, leading to better training and inference.

Significance: In addition to achieving state-of-the-art results, this proposed approach could also be adapted for locating regions of interest in other physiological signals, such as respiration, blood pressure, or muscle activity.

We present a fully open-source, air-driven bidirectional, rotational MRI phantom. It enables an accurate and reproducible evaluation of displacement artefacts for any MRI sequence and velocity field and acceleration sensitivity for phase-contrast MRI (PC-MRI) sequences. Its unique feature of analytically defined motion is expected to narrow the gap between simulations and experiments for in-silico and in-vitro experiments using the very same sequence code.

Methods: A rotational phantom was bidirectionally driven (clockwise (CW) / counterclockwise (CCW)) by an actively controlled airflow. The rotating cylinder filled with a Polyvinylpyrrolidone-water mixture was monitored via an external laser-based tachometer system. Vendor-supplied and custom open-source PC-MRI sequences were evaluated on a 3T MRI system and used as input for Bloch simulations. Resulting magnitude and velocity images were evaluated against the phantom's ground truth data.

Results: For physiological angular velocities, displacement errors resulted in a 10% radial stretch while apparent acceleration sensitivity is 5% of v_enc. The time difference between velocity and spatial encoding time points of 1.9ms determining the severity of the artefacts could be quantified without prior knowledge of details about the MR sequence. Simulation and experiment yielded excellent agreement.

Conclusion: The phantom enables an easy, precise and repeatable evaluation of motion sensitivity of MR sequences and may offer a future reference measurement. Additional timing parameters of MR sequences may be reported in future literature to improve comparability. The seamless MRI sequence definition for in-silico and in-vitro experiments narrows a significant gap in MR research.

Significance: This work establishes a reproducible, standardized validation framework for PC-MRI techniques that can be readily implemented across institutions, facilitating quality assurance procedures and supporting the development of more accurate flow quantification methods in clinical applications.

Objective: To develop a real-time method for designing gradient waveforms for arbitrary k-space trajectories that are time-optimal and hardware-compliant.

Methods: The gradient waveform is solved recursively under both the slew-rate and the trajectory constraints, which form a quadratic equation. The gradient constraint is enforced by thresholding the L2-norm of the gradient vectors. To ensure the existence of the solution, gradient magnitude is thresholded by the escape velocity. A Discrete-Time Forward and Backward Sweep strategy is then applied to further constrain the slew-rate. Trajectory and gradient reparameterization strategies are adopted to enhance the generality and preserve the sampling accuracy. The proposed method is compared with the conventional optimal control method across seven commonly adopted non-Cartesian trajectories. Imaging feasibility of the designed time-optimal gradient waveform was demonstrated by phantom and in vivo imaging experiments.

Results: The proposed method achieves a >89% reduction in computation time and a >98% reduction in slew-rate error simultaneously. The computation time of the proposed method is shorter than the gradient duration for all tested cases, validating the real-time capability of the proposed method.

Conclusions: The proposed method enables real-time and hardware-compliant gradient waveform design, achieving significant reductions in computation time and slew-rate overshoot compared to the previous method.

Significance: This is the first method achieving real-time gradient waveform design for arbitrary k-space trajectories.

Objective: The study seeks to determine whether a powered, cable-driven exosuit has the potential to lower the lumbar muscle activity and overall metabolic expenditure of symmetric and asymmetric lifting tasks.

Methods: A lightweight, cable-driven back exosuit, using a three-state impedance controller, was developed to provide variable assistance based on user posture. Experimental electromyography (EMG), metabolic cost, and user preference data were recorded for ten participants evaluated wearing the powered back exosuit versus the backX, a commercially available passive back support exoskeleton, and a no exo baseline.

Results: Both exoskeletons significantly reduced (p$< $0.05) muscle activation of certain lumbar flexor and extensor muscles when compared to a no exo condition across all conditions tested, though neither significantly reduced the metabolic cost associated with lifting. Users tended to prefer lifting with the powered device as opposed to the passive or no exo condition.

Conclusion: Despite the increased mass of powered back support exoskeletons, these devices can reduce lumbar muscle activity to a similar degree as passive exoskeletons, and are favored by users over their passive counterparts.

Significance: While current powered back support devices tend to incur the cost of being heavy, rigid, and inconvenient for certain lifting postures, these results show that cable-driven powered devices may minimize these factors to the point that they are favored over the currently popular passive devices on the market.

Brain-computer interface (BCI) technology has significant applications in neuro rehabilitation and motor function restoration, especially for patients with stroke or spinal cord injury. Motor imagery electroencephalog-raphy (MI-EEG) is widely used in BCIs, but its nonlinear dynamics and inter-subject variability limit decoding accuracy. In this paper, a multiscale hybrid attention network (MSHANet) for MI-EEG decoding, which consists of spatiotemporal feature extraction (STFE), talking head self-attention (THSA), dynamic squeeze-and-excitation attention (DSEA), and a temporal convolutional network (TCN), is proposed. MSHANet was evaluated via within-subject experiments using BCI Competition IV Datasets 2a and 2b, as well as EEGMMID, achieving decoding accuracies of 83.56%, 89.75%, and 75.66%, respectively. In cross-subject experiments on the three datasets, the mode lattained accuracies of 69.93% on BCI-2a, 81.85% on BCI-2b, and 79.67% on EEGMMID. In addition, we propose an electrode spatial structure-aware encoder. This technique encodes the spatial positions of electrodes in the original data, enabling the model to obtain richer spatial electrode information at the input stage. In within-subject and cross-subject tasks on BCI-2a, this encoding improved the decoding performance by 2.83% and 2.91%, respectively. Visualization was also employed to elucidate feature distributions and the effec tiveness of its attention mechanisms. Experimental results demonstrate that MSHANet performs exceptionally well in MI-EEG decoding tasks and has high potential for clinical applications, particularly in neurorehabilitation and motor function reconstruction.

Background: Acquiring human- like motor skills in embodied musculoskeletal models is challenging due to the high dimensionality and redundancy of muscle actuators.

Methods: Inspired by human motor control coordination patterns, we introduce a synergy-guided reinforcement learning framework integrating physiological priors derived from muscle synergies into the control policy of an embodied musculoskeletal model. It leverages coordinated muscle activation patterns to guide learning, generating muscle excitation signals via a synergy-guided control component and a residual control component. To evaluate the proposed method, four badminton stroke skills are selected as benchmark tasks (forehand/backhand, inward/outward net slices).

Results: The experimental results demonstrate that our method achieves an average root mean square error of under 0.015 radians across all stroke types, demonstrating its ability to accurately learn expert motion. Furthermore, it outperforms the baseline proximal policy optimization (PPO) model in terms of trajectory accuracy, energy efficiency, and training convergence speed, particularly excelling in energy efficiency with up to a 14.9% reduction in energy consumption. The forehand high serve is also tested to validate the method's effectiveness in learning longer and larger-ranges movements, showing the same advantages. Moreover, the muscle synergies learned by the model exhibit moderate resemblance to human synergies, indicating potential interpretability and biological plausibility.

Conclusion: This work highlights that integrating neurophysiological priors into reinforcement learning provides a promising pathway for efficient, interpretable, and human- like motor control.

Significance: The approach holds promise for advancing motor skill assessment, human-machine interfaces, and rehabilitation technologies by enabling more efficient and human- like motion skill learning.

Objectives: Managing acute heart failure (AHF) involves the dual challenges of stabilizing hemodynamics while minimizing myocardial oxygen consumption. This is a clinically significant and technically challenging issue due to complex cardiovascular interactions and trade-offs between therapeutic goals. We aim to develop a discrete hemodynamic control framework for autonomous drug therapy that enhances both control versatility and clinical potential.

Methods: A comprehensive multi-input, multi-output model was derived to capture causal relationships linking drug infusion, cardiovascular parameters, and hemodynamic responses. Based on this model, we designed an optimal control framework capable of regulating arbitrary hemodynamic targets (e.g., arterial pressure, cardiac output, left atrial pressure, right atrial pressure), minimizing myocardial oxygen consumption, accommodating multiple guideline-concordant drug classes, and enforcing clinical constraints such as dosage limits and contraindications. The discrete design enables compatibility with standard hemodynamic measurements in ICU/CCU settings. Control performance was validated using a cardiovascular simulator.

Results: The system achieved multi-dimensional regulation of hemodynamics and reduced myocardial oxygen consumption across a range of AHF scenarios. It also identified pharmacologically untreatable cases by evaluating the reachable hemodynamic range. Performance remained stable despite inter-patient variability in drug sensitivities, circulatory properties, and baroreflex activity.

Conclusions: The proposed system may function as a physician-assistive tool and a foundation for autonomous drug therapy in AHF.

Significances: By integrating physiologically grounded modeling with practical clinical constraints, the system offers a novel and scalable approach to optimize drug therapy in AHF. It has the potential to improve treatment precision, reduce clinician burden, and advance next-generation critical care.

The electrocardiogram (ECG) is a vital diagnostic tool used to monitor and diagnose a wide range of cardiac conditions. However, ECG signals can exhibit significant variability across different patient populations, recording devices, and environmental conditions, creating challenges in developing universally robust and accurate classification models. This research addresses these challenges by exploring and advancing domain adaptation techniques to enhance the robustness and generalizability of ECG classification models. By leveraging unsupervised domain adaptation (UDA), we aim to mitigate the performance degradation that typically occurs when models trained on one dataset are applied to another, thereby improving diagnostic accuracy and reliability across diverse clinical settings. We introduce ECG-Adapt, an integrated approach that aligns features both within classes and across domains. Unlike existing methods that rely on clustering as a preprocessing step, ECG-Adapt does not require clustering, simplifying the workflow. It further incorporates weakly supervised learning to prevent overfitting of the discriminator to pseudo-labels generated by the classifier, enhancing robustness and generalizability. Applying our novel unsupervised domain adaptation framework led to substantial performance gains. For instance, ECG-Adapt improved the average $F_{1}$-score by 8% on single-lead problems and 7% on 12-lead problems. By leveraging ECG-Adapt, performance degradation when applying models across datasets can be mitigated, enhancing diagnostic accuracy and reliability in diverse clinical settings and demonstrating strong potential for real-world deployment.

Objective: Upper-limb electrocardiogram (ECG) acquired from a single-arm wearable device offers a practical approach for dynamic cardiac monitoring. This study addresses whether single-channel Arm-ECG, characterized by higher noise and distinct morphology compared to standard 12-lead ECG, can reliably detect arrhythmias.

Methods: We propose CLMF-Net, a contrastive multitask framework featuring multiscale convolutional layers to capture temporal-morphological patterns. A fine-grained reconstruction branch preserves subtle clinical features, including P waves and ST segments, while a contrastive module ensures robustness against signal quality variations. The complete framework is trained end-to-end using a unified loss function that balances classification, reconstruction, and consistency objectives.

Results: Validation with 132 elderly participants undergoing six-minute walk tests confirmed the feasibility of dynamic Arm-ECG acquisition. CLMF-Net, trained for arrhythmia identification from single-channel ECG, achieved 95.19% accuracy on the Arm-ECG dataset. It effectively detects arrhythmias, despite challenges with noise and morphology. Generalizability was evidenced by 95.55% and 84.23% accuracy on the Chapman and China Physiological Signal Challenge 2018 (CPSC2018) datasets, respectively, demonstrating the model's ability to classify multiple arrhythmia types across diverse datasets.

Conclusion: Beyond precise identification of arrhythmias, deep feature and morphology analyses showed that the learned representations do not always align with clinically emphasized intervals. This discrepancy highlights both the promise of leveraging Arm-ECG signals and the caution required when translating model-derived interpretations into clinical workflows.

Significance: This study validates the clinical utility of single-arm ECG monitoring, demonstrating feature reliability for real-world cardiac health assessment and supporting the translational potential through consistency with standard recordings.

Objective: Inter-session and inter-subject variability in electroencephalography (EEG) signals, resulting from individual differences and environmental factors, poses a major challenge for neural decoding in brain-computer interface (BCI) applications.

Methods: To address this issue, we propose RUNet, a zero-calibration motor imagery EEG decoding framework based on Riemannian manifold learning and unsupervised representation learning. RUNet incorporates a multi-scale spatiotemporal convolutional module that jointly captures local global spatial and multi-resolution temporal dynamics features. To enhance the robustness of EEG features against non stationarity, a polysynergistic covariance optimization module is employed, which strengthens the covariance matrix representation through multiple regularizations and adaptive fusion. In addition, RUNet integrates the Riemannian Affine Log Mapping layer, based on Affine-Invariant Transformation and Log-Euclidean Mapping, in an end-to-end manner to mitigate cross-domain covariance drift and enhance domain-invariant feature learning. A transfer learning framework is further integrated into RUNet: during pre-training, an unsupervised contrastive loss is applied to resting-state EEG data to learn domain-invariant spatiotemporal features; during retraining, task-specific data are used to enhance discriminability and feature disentanglement.

Conclusion: Experimental results on the BCI Competition IV 2a, 2b datasets and a self-collected laboratory dataset show that RUNet achieves average cross-session accuracies of 87.19%, 88.03% and 85.45%, and cross-subject accuracies of 68.09%, 78.29% and 87.25%, respectively. On the PhysioNet dataset, a cross-subject accuracy of 78.14% is achieved. These results demonstrate the effectiveness of RUNet's unified pipeline and its robust cross-domain generalization.