Biomedical Engineering Letters最新文献

To investigate the biomechanical effects of medial collateral ligament (MCL) stiffness adjustments on knee kinematics-medial femoral rollback, femoral rotation, and joint contact forces-in mechanically aligned posterior-substituting (PS) total knee arthroplasty (TKA). A musculoskeletal model simulating squatting was developed using the AnyBody modeling system. A PS-TKA prosthesis was implanted, and MCL stiffness was modified in 20% increments. The effects on femoral rollback, femoral rotation, and joint forces were evaluated. Medial femoral rollback was not significantly affected by changes in MCL stiffness. However, when MCL stiffness exceeded 20% above normal, the pattern and magnitude of lateral femoral rollback were altered compared to other conditions. Increased MCL stiffness also altered internal-external femoral rotation and raised joint contact forces in the medial compartment. Muscle activity was largely unaffected by changes in MCL stiffness, although hamstring activity increased slightly during early flexion (0°-5°) when MCL stiffness exceeded 20%. Excessive MCL stiffness (over 20% above normal) affects lateral femoral rollback and increases joint contact forces, potentially elevating the risk of prosthetic wear. Maintaining MCL stiffness within physiological limits is critical for optimizing outcomes in varus knee TKA.

Sarcopenia is a rapidly rising health concern in the fast-aging countries, but its demanding diagnostic process is a hurdle for making timely responses and devising active strategies. To address this, our study developed and evaluated a novel sarcopenia diagnosis system using Stimulated Muscle Contraction Signals (SMCS), aiming to facilitate rapid and accessible diagnosis in community settings. We recruited 199 adults from Wonju Severance Christian Hospital between July 2022 and October 2023. SMCS data were collected using surface electromyography sensors with the wearable device exoPill. Their skeletal muscle mass index, handgrip strength, and gait speed were also measured as the reference. Binary classification models were trained to classify each criterion for diagnosing sarcopenia based on the AWGS cutoffs. The binary classification models achieved high discriminative abilities with an AUC score near 0.9 in each criterion. When combining these criteria evaluations, the proposed sarcopenia diagnosis system performance achieved an accuracy of 89.4% in males and 92.4% in females, sensitivities of 81.3% and 87.5%, and specificities of 91.0% and 93.8%, respectively. This system significantly enhances sarcopenia diagnostics by providing a quick, reliable, and non-invasive method, suitable for broad community use. The promising result indicates that SMCS contains extensive information about the neuromuscular system, which could be crucial for understanding and managing muscle health more effectively. The potential of SMCS in remote patient care and personal health management is significant, opening new avenues for non-invasive health monitoring and proactive management of sarcopenia and potentially other neuromuscular disorders.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-025-00461-z.

Purpose: Unethical attempts to misuse and overdose opioids have led to strict prescription limits, necessitating frequent hospital visits and prescriptions for long-term severe pain management. Therefore, this study aimed to develop a prototype wearable device that facilitates the extended delivery of opioid drugs while incorporating abuse-deterrent functionality, referred to as the abuse deterrent device (ADD).

Methods: The ADD was designed and fabricated using 3D-printed components, including reservoirs for the drug and contaminant, as well as an actuator. In vitro tests were conducted using a skin-mimicking layer and phosphate-buffered saline (PBS) to evaluate the drug release profile and the effectiveness of the ADD abuse-deterrent mechanism.

Results: Under simulated skin attachment, ADD demonstrated sustained drug release with the potential to persist for up to 200 days. Upon detachment from the skin mimic, the mechanical components of the ADD facilitated immediate exposure of the contaminant to the drug and effectively halted further drug exposure throughout-diffusion.

Conclusion: Wearable ADD provides a secure and practical solution for the long-term treatment of high-risk medications such as opioids, enhances patient convenience, and addresses important public health concerns.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-025-00459-7.

The automated identification of the first and second heart sounds (S1 and S2, respectively) in phonocardiogram (PCG) signals plays a pivotal role in the detection of heart valve diseases based on the known occurrence of heart murmurs between S1-S2 or S2-S1 in valve disorders. Traditional neural network-based methods cannot differentiate between heart sounds and background noise, leading to decreased accuracy in the identification of crucial cardiac events. Therefore, a deep learning-based segmentation on PCG signals that can distinguish S1 and S2 heart sounds with the Convolutional Fourier transform (CF) modules, which are two sequentially connected CF modules, was proposed in this study. Internal datasets, alongside the publicly available PhysioNet 2016 dataset, were used for the training and validation of the CF modules to ensure a robust comparison against existing state-of-the-art models, specifically the logistic regression-Hidden semi-Markov model (LR-HSMM). The efficacy of the CF modules was further evaluated using external datasets, including the PhysioNet 2022 and the Asan Medical Center (AMC) datasets. The CF modules exhibited superior robustness and accuracy in segmenting S1 and S2, achieving an average F1 score of 97.64% for S1 and S2 segmentation, which indicated better performance compared with that of the previous best model, LR-HSMM. The integration of the CF modules ensures the robust performance of PCG segmentation even amidst heart murmurs and background noise, significantly contributing to the advancement of cardiac diagnostics. All code is available at https://github.com/mi2rl/PCG_FTseg.

Purpose: Hair loss affects significant social and psychological well-being issues of the person. Thus, various drugs, ingredients, and technologies are being developed to overcome it. Minoxidil (MXD) is a representative hair loss treatment drug because it suppresses the production of dihydrotestosterone and induces vasodilation. However, since MXD has various side effects when used orally, it is more desirable to use it topically. In this work, we disclosed a new polymeric formulation (MXD@CP) based on citric acid (CA) dimethylsiloxane polymer (CP) for the effective transdermal delivery of MXD.

Methods: The polymer that induced ring-opening polymerization based on CA was named CA-siloxane polymer (CP). After CP synthesis, MXD was loaded onto CP to form MXD@CP. The formed MXD@CP was confirmed to have efficacy as a transdermal delivery system through various material property analyses and biotoxicity and therapeutic efficacy analyses.

Results: In these results, CP stably loaded MXD up to 5%, a concentration used in clinical practice, and showed higher hair growth efficacy and hair follicle formation efficacy compared to MXD@PBS.

Conclusion: In the animal study, MXD@CP showed a superior hair growth effect which suggests its potential as a next-generation hair loss treatment agent.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-025-00460-0.

Electrocardiogram (ECG) is mainly utilized for diagnosing heart diseases. However, various noises can influence the diagnostic accuracy. This paper presents a novel algorithm for denoising ECG signals by employing the Controlled Energy Constraint-Variational Mode Decomposition (CEC-VMD). Firstly, the noisy ECG signal is decomposed using CEC-VMD to obtain a set of intrinsic mode functions (IMFs) and a residual r. A modulation factor is utilized to minimize the modal information contained in the decomposed residuals. Furthermore, this paper presents an update formula for the modal and central frequencies based on ADMM. Finally, all the IMFs are integrated to obtain the ECG signal after denoising. By varying the value of the modulation factor, not only is the spectral energy loss of each mode reduced, but the orthogonality between the modes is also improved to better concentrate the energy of each mode. The experiments on simulated signals and MIT-BIH signals show that the average SNR after CEC-VMD denoising is 22.5139, the RMSE is 0.1128, and the CC is 0.9882. In addition, the proposed algorithm effectively improves the classification accuracy values, which are 99.0% and 99.9% for the SVM and KNN classifiers, respectively. These values are improved compared with those of EMD, VMD, SWT, SVD-VMD, and VMD-SWT. The proposed CEC-VMD technique for denoising ECG signals removes noise and better preserves features.

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.