Biomedical Engineering Letters最新文献

Positron Emission Tomography (PET) systems with high spatial resolution and sensitivity suffer from reduced photon transmittance due to the high aspect ratio of scintillation crystals and the large refractive index (RI) difference at the crystal-photosensor boundary. This study aimed to enhance light extraction from the scintillation crystal to the photosensor by applying various nanopatterns on the crystal surface. Various nanopattern shapes, including line, circular, hexagonal, and tapered pyramid, were designed and simulated using Monte Carlo and finite-difference time-domain (FDTD) methods. The optimization focused on the nanostructure's diameter, width, height, period ratio, and RI. Light extraction gain was evaluated against a reference dataset with a 100 nm thick airgap between the crystal and photosensor. Nanopatterns significantly improved light transmission at the crystal-photosensor boundary, especially for scintillation photons entering at angles larger than the critical angle. Hole-type patterns showed superior performance with lower heights, larger period ratios, and RIs between 1.7 and 1.9. A maximum light extraction gain of 1.46 was achieved with a hole-type circular nanopattern with an RI of 1.7. Furthermore, our simulation results were experimentally validated through the preliminary development of a nanopattern applied to the GAGG crystal. Nanopattern on the crystal surface can effectively enhance light extraction to the photosensor. These findings were experimentally validated, confirming the potential of nanopatterns in improving PET system performance.

In chronic respiratory diseases, continuous self-monitoring of vital signs such as respiratory rate aids in the early detection of exacerbations. In recent years, the development of smart clothing, such as garments equipped with sensors to measure respiratory rate, has been a focus of research. However, the usability and adoption of smart clothing are often compromised owing to the discomfort caused by compression pressure during wear. This study developed smart clothing designed to measure respiratory rate using a low compression pressure. This was achieved by integrating a bending angle sensor, based on double-layer capacitance, into the rib cage and abdomen areas. The accuracy of the respiratory rate measurement was evaluated in 20 healthy male subjects without respiratory diseases. Breathing was measured while the subjects wore the smart clothing and performed breathing exercises in sitting, supine, and lateral postures, following a metronome set between 12 and 30 bpm. To assess accuracy, the respiratory rate measured by the smart clothing was compared with that measured by a spirometer. The recorded compression pressure was 0.77 ± 0.21 kPa, with no subjects reporting discomfort. Correlation coefficients for respiratory rate in the different postures ranged within 0.97-0.99. The mean difference between the smart clothing and spirometer measurements was less than 0.1 bpm. The low mean difference indicated that the proposed low compression pressure wearable respiration sensor, employing a bending angle sensor based on double-layer capacitance, could measure respiratory rate accurately without causing discomfort and within an acceptable error range.

The concept of temporal visual attention in dynamic contents, such as videos, has been much less studied than its spatial counterpart, i.e., visual salience. Yet, temporal visual attention is useful for many downstream tasks, such as video compression and summarisation, or monitoring users' engagement with visual information. Previous work has considered quantifying a temporal salience score from spatio-temporal user agreements from gaze data. Instead of gaze-based or content-based approaches, we explore to what extent only brain signals can reveal temporal visual attention. We propose methods for (1) computing a temporal visual salience score from salience maps of video frames; (2) quantifying the temporal brain salience score as a cognitive consistency score from the brain signals from multiple observers; and (3) assessing the correlation between both temporal salience scores, and computing its relevance. Two public EEG datasets (DEAP and MAHNOB) are used for experimental validation. Relevant correlations between temporal visual attention and EEG-based inter-subject consistency were found, as compared with a random baseline. In particular, effect sizes, measured with Cohen's d, ranged from very small to large in one dataset, and from medium to very large in another dataset. Brain consistency among subjects watching videos unveils temporal visual attention cues. This has relevant practical implications for analysing attention for visual design in human-computer interaction, in the medical domain, and in brain-computer interfaces at large.

This study aims to establish a methodology for classifying gait patterns in patients with hip osteoarthritis without the use of wearable sensors. Although patients with the same pathological condition may exhibit significantly different gait patterns, an accurate and efficient classification system is needed: one that reduces the effort and preparation time for both patients and clinicians, allowing gait analysis and classification without the need for cumbersome sensors like EMG or camera-based systems. The proposed methodology follows three key steps. First, ground reaction forces are measured in three directions-anterior-posterior, medial-lateral, and vertical-using a force plate during gait analysis. These force data are then evaluated through two approaches: trend similarity is assessed using the Pearson correlation coefficient, while scale similarity is measured with the Symmetric Mean Absolute Percentage Error (SMAPE), comparing results with healthy controls. Finally, Gaussian Mixture Models (GMM) are applied to cluster both healthy controls and patients, grouping the patients into distinct categories based on six quantified metrics derived from the correlation and SMAPE. Using the proposed methodology, 16 patients with hip osteoarthritis were successfully categorized into two distinct gait groups (Group 1 and Group 2). The gait patterns of these groups were further analyzed by comparing joint moments and angles in the lower limbs among healthy individuals and the classified patient groups. This study demonstrates that gait pattern classification can be reliably achieved using only force-plate data, offering a practical tool for personalized rehabilitation in hip osteoarthritis patients. By incorporating quantitative variables that capture both gait trends and scale, the methodology efficiently classifies patients with just 2-3 ms of natural walking. This minimizes the burden on patients while delivering a more accurate and realistic assessment. The proposed approach maintains a level of accuracy comparable to more complex methods, while being easier to implement and more accessible in clinical settings.

Deep learning has demonstrated remarkable performance across various domains. One of the techniques contributing to this success is data augmentation. The essence of data augmentation lies in synthesizing data while preserving accurate labels. In this research, we introduce a data augmentation technique optimized for electrocardiogram (ECG) data by focusing on the unique angles between precordial leads in 12-lead ECG, considering situations that may occur in a clinical environment. Subsequently, we utilize the proposed data augmentation technique to train a deep learning model for diagnosing atrial fibrillation or atrial flutter, generalized supraventricular tachycardia, first-degree atrioventricular block, left bundle branch block and myocardial infarction from ECG signals, and evaluate its performance to validate the effectiveness of the proposed method. Compared to other data augmentation methods, our approach demonstrated improved performance across various datasets and most tasks, thereby showcasing its potential to enhance diagnostic accuracy. Additionally, our method is simple to implement, offering a gain in total training time compared to other augmentation methods. This study holds the potential to positively advance further development in the fields of bio-signal processing and deep learning technology, addressing the issue of the lack of optimized data augmentation techniques applicable to ECG data in the future.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-024-00455-3.

While tripping is the leading cause of injurious falls in older adults, the influence of ankle and hip proprioceptive information in balance recovery among older adults is still not clearly understood. The objective of this study was to assess the influence of ankle vs. hip proprioceptive information by altering muscle spindle performance using vibratory stimulation among older adults and healthy young control participants. Two groups of young (n =  20, age =  22.2 ± 3.1 years) and older adult (n  =  33, age = 74.0 ± 3.8 years) participants were recruited and went through treadmill perturbation (sudden backward treadmill movement mimicking a trip), while they were equipped with vibratory devices (no vibration, and 40 and 80 Hz) on either ankle or hip muscles. Kinematics of the recovery were measures using motion sensors on lower extremities and the trunk. Results showed that vibratory stimulation on ankle significantly influenced balance recovery response (i.e., increased reaction time by 18% and increased recovery step length by 21%) among healthy young control, while it showed no effect when placed on hip muscles. On the other hand, while vibratory stimulation on ankle showed no effect on balance recovery among older adults, it significantly influenced balance recovery when applied to the hip muscles (i.e., increased reaction time by 12% and increased recovery step length by 10%). Current findings suggest that the role of ankle vs. hip proprioceptive information in balance recovery may change by aging. Findings may potentially be used for targeting the appropriate location for balance interventions and reducing the fall risk in older adults.

Influenza-like illnesses (ILI), such as influenza and RSV, pose significant global health burdens, especially in febrile children under 6 years old. Differentiating these from bacterial infections based solely on clinical symptoms is challenging. While PCR tests are reliable, they are costly and time-consuming. An effective predictive tool would help doctors prioritize tests and guide parents on seeking emergency care for their febrile children. We collected data from 2,559 children who visited the hospital for ILI inspections. We developed XGBoost models, comparing nine different machine learning algorithms. Our AI-assisted diagnostic pipeline consists of two stages: Decision Support System for patients (DSS-P): An in-house model using sex, age, symptoms, and medical history to decide on hospital visits. Decision Support System for clinicians (DSS-C): An in-hospital model incorporating breath sound types and Chest X-ray results to determine the necessity of clinical tests. We tested various experimental settings, including the addition of RAT-tested samples and the combined consideration of influenza and RSV. The performance for influenza achieved an Area Under the Curve of 0.749 and 0.776, while RSV achieved 0.907 and 0.924 in DSS-P and DSS-C, respectively. We identified biomarkers, noting that most biomarkers had opposite effects for influenza and RSV. This study developed predictive models for influenza and RSV and explored their underlying mechanisms. An expectation tool to guide doctors in prioritizing tests or assisting parents in deciding on emergency care for their febrile child would be invaluable. Biomarker analysis performed can provide insight on clinical fields.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-024-00450-8.

Purpose: Given objective benefits of robotic-assisted sacrocolpopexy (RSCP) to the voiding function/deficit of patients with pelvic organ prolapse (POP) waits to be clarified, this study investigated if RSCP modifies voiding functions of POP patients by focusing on its impact on the outlet resistance-dependent voiding workload using pressure-volume analysis (PVA), a protocol thermodynamically assaying work expenditure by the bladder in voiding cycles.

Methods: Pre- and post-operative cystometry and PVA of 22 female patients, who underwent RSCP for POP (stage ≥ II), were reviewed. Mean voiding resistance (Rvod), mean voiding pressure (Pvod), mean voiding flow (Fvod), voided volume (Vvod), voiding time (Tvod), and the trajectory-enclosed area (Apv) were analyzed.

Results: The PVA, in which trajectory shaped an enclosed loop representing a voiding cycle, was established by adapting from the time-domain cystometry. Compared to the pre-operative control, RSCP decreased Rvod, Pvod, and Tvod (p = 0.003, 0.042, and 0.040, respectively. All N = 22) but increased Fvod (p = 0.036, N = 22) without markedly affecting Vvod (p = 0.580, N = 22). Apv was decreased after RSCP (p = 0.017, N = 22). The RSCP-decreased Rvod (ΔRvod) displayed a moderate correlation with both the decreased Pvod (ΔPvod, r = 0.551, p = 0.007, N = 22) and the increased Fvod (ΔFvod, r=-0.625, p = 0.001, N = 22). The ΔFvod moderately correlated with the decreased Tvod (ΔTvod, r=-0.620, p = 0.002, N = 22). Moreover, the RSCP-decreased Apv (ΔApv) displayed correlation with the ΔPvod (r = 0.385, p = 0.047, N = 22).

Conclusions: Through diminishing outlet resistance of POP patients, RSCP not only prompted urine emission thereby increased voiding efficacy but also decreased the pressure developed for driving urine flow that lessened voiding workload.Clinical Trial Registration ClinicalTrials.gov (NCT05682989).

This study aims to determine whether the removal of the femoral neck system (FNS) after bony union affects the biomechanical stability of the femur. Considering the technical challenges and potential complications, including screw stripping reported in recent studies, the study explores whether its removal impacts stress distribution within the femur and increases the risk of complications, such as screw stripping. The femurs were grouped into Intact, Group U (healed fractures with FNS in place), and Group R (healed fractures with FNS removed). Subgroup analysis was performed using Pauwels' classification for fractures at 30, 50, and 70 degrees. Finite element analysis (FEA) was used to model and evaluate the biomechanical behavior. Material properties for the models were derived from the literature. No significant difference in biomechanical stability was observed between Group U and Group R across the fracture angles tested, indicating that removal of FNS does not compromise the structural integrity of the femur. However, the risk of screw stripping during removal requires consideration. Removing the femoral neck system (FNS) after fracture healing preserves the femur's biomechanical stability, regardless of fracture angle. However, increased stress at the distal locking screw suggests caution to avoid complications such as screw stripping.

This paper demonstrates real-time, label-free, contact-based glucose sensor design of inset-fed Microstrip Patch Antenna (MSPA) genres: Slotted Microstrip Patch Antenna (SMSPA) and Through-hole Microstrip Patch Antenna (THMSPA). In SMSPA, multiple slots are created along the width edge of the patch. In THMSPA, a through-hole is introduced across the antenna including all the layers: patch, substrate and ground conductor of the MSPA. The proposed designs are geared towards enhancing the electric field distribution along the patch, and to utilize that region as the sensing area. The electric field intensity at the resonant frequency is 45505V/m, 53145V/m and 71348V/m for MSPA, SMSPA and THMSPA, respectively. Experimental sensitivity of the proposed glucose sensor increased from 8.901dB/g/ml to 23.575dB/g/ml and 41.525dB/g/ml for SMSPA and THMSPA, respectively. There is significant enhancement in sensitivity in terms of MHz/g/ml for MSPA, SMSPA and THMSPA which is 112.286, 174.857 and 548.571, respectively.