Abstracts:Current ultrasound sensors used for wearable blood pressure monitoring typically require driving voltages above 15 V. This study aims to enhance device sensitivity to enable low-voltage operation, thereby reducing power consumption and improving user safety. We propose an ultrathin curved piezoelectric micromachined ultrasonic transducer (PMUT) featuring a 0.5 µm aluminum nitride (AlN) film, which exhibits a resonance frequency of 7.5 MHz in water. The curved PMUT array exhibits a 1.8× improvement in unit-area transmitting sensitivity and a 2.6× enhancement in unit-area echo sensitivity compared to a planar PMUT array fabricated with the same piezoelectric material. Continuous in vivo monitoring of radial artery diameter was performed using a 2.5 V driving voltage, validating its feasibility for noninvasive blood pressure measurement. The proposed curved PMUT offers an innovative solution for wearable ultrasonic sensing, featuring high sensitivity, low power consumption, and enhanced circuit integration, positioning it as a promising candidate for next-generation physiological monitoring devices.

Abstracts:In this study, a novel methodology is proposed, combining Reconstructed Phase Space (RPS) analysis with an optimized Delay State Network (DSN) to enhance the detection and classification of cardiac arrhythmias. Traditional methods often fail to capture subtle temporal phase drifts indicative of arrhythmias or require extensive computational resources and handcrafted features, limiting their effectiveness for early diagnosis and real-time applicability. The proposed approach reconstructs the nonlinear dynamics of cardiac signals and leverages the entire Phase Space Structure (PSS) as direct input to the DSN. The optimized DSN employs a single nonlinear node with delayed feedback to emulate multiple virtual nodes, reducing hardware demands by over an order of magnitude compared to conventional reservoirs or LSTMs. To accurately capture ECG dynamics, the framework integrates delay and embedding optimization, while PCA and Ridge Embedding manage dimensionality within the DSN. The functionality of the DSN model is further optimized by incorporating shared memory and multiprocessing frameworks, enabling scalable and efficient handling of large datasets. The methodology was validated on three benchmark datasets, demonstrating its generalizability across diverse cardiac conditions. Experimental results achieved 99.3% accuracy, with sensitivity and specificity of 99.1% and 99.7%, respectively. Edge deployment on a Raspberry Pi 5 demonstrated inference within $1.2\!-\!4.8$ seconds for $ 10\,s\!-\!60\,s$ ECG segments, with peak memory usage of 2.57 GB observed for 60 s segments, and power consumption remaining below 2.5 W. The proposed framework provides a robust, scalable, and accurate solution for arrhythmia classification and broader time-series-based diagnostics in resource-constrained environments.

Abstracts:Objective: Imposing accurate outflow boundary conditions remains a significant challenge in 3D computational fluid dynamics simulations of patient-specific cerebral blood flow. Widely used Windkessel models often rely solely on geometric factors, such as outlet numbers and diameters, leading to inaccuracies caused by image quality limitations and simplified vessel representations. This preliminary study proposes a novel functional region-based approach to enhance the accuracy of cerebral blood flow simulations. Methods: Cerebral vessels were divided into functional regions by combining population-based cerebral blood flow distributions with patient-specific arterial geometries from medical images. Within each functional region, parameters of Windkessel models for individual outlets are calculated based on their corresponding diameters/areas, accounting for both functional and geometric characteristics. Validation was conducted on a single subject using clinical Transcranial Doppler ultrasound data, with comparisons made to a conventional area-based approach. Results: The functional region-based approach demonstrated better alignment with clinical measurements, outperforming the area-based method in velocity profiles at 5 of 7 monitored locations. It also provided closer agreement with measured blood flow distribution, with maximum percentage differences of −4.5%. Conclusion: By integrating vascular geometry and functional perfusion data, the proposed approach provides a physiologically informed strategy for setting outlet boundary conditions in cerebral blood flow simulations. Significance: Although demonstrated in a single-subject case, this approach shows potential to improve patient-specific simulation reliability by reducing errors caused by imaging artifacts and geometric simplifications, offering value for future clinical and research applications.

Abstracts:Objective: This work proposes a method to estimate the kinematic parameters of multi-joint systems where direct measurement is infeasible, such as joints of the hand. Methods: Our novel data-driven estimation method uses “workspace manifold mapping” that relies on a unique geometry that arises in exponential representations of 3D motions of two rigid bodies connected via one- or two-degree-of-freedom (DOF) joints. We describe and verify our “Generative Topographic Mapping algorithm with kinematic constraints” (GTM-KC) using simulated data and motion capture data for a 2-DOF bio-inspired mechanical linkage. We compare the performance of GTM-KC to several benchmark algorithms. Results: Upon applying GTM-KC to motion capture data of a bio-inspired linkage, the mean estimates of the 2-DOF joint axis orientations deviated from ground truth by 2.5° with a standard deviation of 3.4° for one axis and by 2.4° with a standard deviation of 2.7° for the second axis. Conclusion: Our GTM-KC method can be used to estimate the orientations of revolute joint axes that link the 3D kinematics of two rigid bodies, and either outperforms or is equivalent to existing methods in terms of accuracy, precision, and reliable convergence to a solution. Notably, the GTM-KC method outperforms existing methods in terms of robustness to initial conditions. Significance: Workspace manifold mapping provides improved kinematic parameter estimation as compared to existing benchmark methods, and can be applied to any 1- or 2-DOF kinematic relationship without loss of generality.

Abstracts:Objective: High-density surface electromyography (HD-sEMG) has emerged as a powerful tool for myoelectric control and activation pattern analysis. However, signal loss due to poor electrode contact and channel corruption remains a significant challenge, limiting the reliability and practical applications of HD-sEMG signals. Conventional interpolation methods fail to effectively reconstruct corrupted signals, especially when multiple adjacent channels are affected. Methods: This paper proposes a novel HD-sEMG signal reconstruction approach based on the denoising diffusion probabilistic model (DDPM) with a repaint strategy. By leveraging a U-Net structure with spatiotemporal embedding modules that effectively learn the spatial and temporal characteristics of HD-sEMG signals, the proposed method achieves high-fidelity signal reconstruction without requiring prior knowledge of corruption patterns. Results: Experimental evaluations are conducted on 6 corruption patterns with varying ratios (from 12.5% to 50%) using self-collected datasets (including an amputated subject) and a benchmark dataset. Results demonstrate that the proposed approach consistently outperforms interpolation methods (linear: 0.038 $\pm$ 0.033, cubic: 0.038 $\pm$ 0.032), generative adversarial net (GAN) (0.049 $\pm$ 0.041), and variational autoencoder (VAE) (0.068 $\pm$ 0.046) in terms of $nRMSE$ ($p < 0.001$), achieving the lowest error of 0.027 $\pm$ 0.027 averaged across all corruption ratios. For $PSNR$, the proposed approach achieves the highest mean value (35.81 $\pm$ 17.95 dB) compared to interpolation methods (linear: 33.89 $\pm$ 26.85, cubic: 33.88 $\pm$ 26.88 dB), GAN (31.08 $\pm$ 19.14 dB), and VAE (26.98 $\pm$ 18.94 dB) ($p < 0.001$). Furthermore, the proposed method maintained robust classification accuracy, achieving statistically equivalent performance to ground truth at the lower corruption ratio. Significance: The proposed HD-sEMG signal reconstruction approach offers a new solution for enhancing the fidelity and reliability of HD-sEMG signal acquisition.

Abstracts:Objective: Electroencephalography (EEG)-based Rapid Serial Visual Presentation (RSVP) has steadily gained attention since 2015 as a paradigm to enhance image target detection in brain-computer interfaces (BCIs) used with healthy individuals. Methods: We reviewed the literature using Scopus and Web of Science as primary databases, covering publications from 2015 to 2024. After literature screening and filtering, a total of 86 papers on RSVP-BCI studies were analyzed over this decade-long period. The research categorizes RSVP into three dimensions: public datasets, paradigm encoding, and decoding methods, while exploring eight mode combinations involving target types, subject groups, and different modalities. Results: Our literature search revealed a scarcity of studies addressing diverse target types across different subject groups or modality combinations, indicating a promising direction for future RSVP-BCI development. Future efforts should prioritize inclusivity across all age groups, the design of user-friendly stimulus interfaces, and the development of advanced algorithms, with the goal of creating a more widely accessible RSVP-BCI system. Conclusion: We have provided a comprehensive review of advances over the past decade in RSVP-based target detection, including datasets, encoding design, decoding methods and potential applications. Significance: The present work aims to articulate prospective trajectories for the continued advancement of the RSVP community.

Abstracts:Objective: Transcranial ultrasound applications require accurate simulations to predict intracranial acoustic pressure fields. The current gold standard typically consists of calculating a longitudinal ultrasound wave propagation using a fluid skull model, which is based on full head CT images for retrieving the skull's geometry and elastic constants. Although this approach has extensively been validated for deep brain targets and routinely used in transcranial ultrasound ablation procedures, its accuracy in shallow cortical regions remains unexplored. In this study, we explore the shear wave effects associated with transcranial focused ultrasound propagation, both numerically and experimentally. The intracranial acoustic pressure was measured at different incidence angles at the parietal and frontal regions in an ex vivo human skull. The fluid-like skull model was then compared to the solid model comprising both longitudinal and shear waves. The results consistently show a larger error and variability for both models when considering an oblique incidence, reaching a maximum of 170% mean deviation of the focal area when employing the fluid skull model. Statistical assessments further revealed that ignoring shear waves results in an average ∼40% overestimation of the intracranial acoustic pressure and inability to obtain an accurate intracranial acoustic pressure distribution. Moreover, the solid model has a more stable performance, even when small variations in the skull-transducer relative position are introduced. Our results could contribute to the refinement of the transcranial ultrasound propagation modeling methods thus help improving the safety and outcome of transcranial ultrasound therapy in the cortical brain areas.

Abstracts:Objective: To measure the steady-state corneal biomechanical properties related to postoperative corneal ectasia, keratoconus, glaucoma and other ophthalmic diseases, we propose a novel in vivo measurement method. Methods: By precisely manipulating ambient negative pressure via a suction device to achieve controlled in vivo corneal inflation, we analyzed the coupling relationship between corneal deformation response and negative pressure loading. The displacement corresponding to the corneal initial configuration under 0 mmHg was extrapolated. The inverse finite element analysis (FEA) technique was employed to calculate the segmental Young's modulus of the cornea under different steady-state intraocular pressure (IOP) conditions. Results: In vivo experiments on 3-month-old rabbit eyes demonstrated a Young's modulus of 0.2–0.3 MPa at physiological IOP (10–12 mmHg), along with an increasing trend of corneal stiffness across a pressure range of 0–85 mmHg. Conclusion: The proposed non-invasive measurement method exhibits stability and consistency in parameter inversion under different IOPs, indicating its significant clinical potential. Significance: Providing a new approach for corneal biomechanical assessment is beneficial for the diagnosis and treatment of ophthalmic diseases.

Abstracts:Objective: Deep neural networks are widely used in the field of optical coherence tomography (OCT) to screen some common retinal diseases. However, for rare diseases with fewer cases for model training, it is challenging to achieve automatic diagnosis using traditional deep learning. Meta-learning based few-shot learning can be used to address the problem of insufficient training data. Methods: We propose a novel algorithm for few-shot OCT image classification, where meta-learning is used to fine-tune the pre-trained model and obtain good initialization for task generalization. Unsupervised learning based on query data is for the first time introduced in meta-learning. Cross-set consistency learning is proposed to reduce the gap between meta-knowledge learned from support and query data. Data mixup is also integrated to generate virtual samples and enhance data variety. Results: A lightweight subset was constructed based on a public OCT dataset and extensive experiments were performed. The classification accuracy of the proposed method was higher than existing few-shot learning methods. To show the generalization of the proposed method, experiments were also performed on a histological image dataset, and superior performance was also achieved. Conclusion: The proposed strategies help the model to fully utilize the limited data and to explore hidden information, improving its generalization to unseen tasks. Significance: The proposed method has great value in training deep learning models for diagnosis of rare diseases.

Abstracts:The patch-clamp technique is the gold standard for electrophysiologists' research into the cellular and molecular biological mechanisms underlying mental activities at the animal level. During the procedure, micropipette trajectory planning plays a significant role in the in vivo patch clamp. However, the high constraint between the cerebral environment and the micropipette's movement, as well as the absence of comprehensive 3D spatial information, make planning its trajectory incredibly challenging. To efficiently avoid blood vessel obstacles and insert into a target destination, this paper proposes an active avoidance micropipette trajectory planning method to improve the efficiency of the micropipette insertion process for in vivo patch clamp. More precisely, a feasible navigable space based on the available spatial information for the micropipette is first developed. The available spatial information is obtained by constructing the three-dimensional vessel distribution within the two-photon microscope imaging field of view. Based on the micropipette's navigable space, a trajectory potential field is then introduced to navigate the micropipette to the destination along the optimized trajectory. Finally, experimental validations and applications demonstrate that our proposed approach increases the success rate and reduces the execution time for the micropipette insertion, as well as minimizes damage to the brain tissue.