Biomedical Engineering Letters最新文献

With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced µV-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications.

Synthesis of a 12-lead electrocardiogram from a reduced lead set has previously been extensively studied in order to meet patient comfort, minimise complexity, and enable telemonitoring. Traditional methods relied solely on the inter-lead correlation between the standard twelve leads for learning the models. The 12-lead ECG possesses not only inter-lead correlation but also intra-lead correlation. Learning a model that can exploit this spatio-temporal information in the ECG could generate lead signals while preserving important diagnostic information. The proposed approach takes leverage of the enhanced inter-lead correlation of the ECG signal in the wavelet domain. Long-short-term memory (LSTM) networks, which have emerged as a powerful tool for sequential data mining, are a type of recurrent neural network architecture with an inherent capability to capture the spatiotemporal information of the heart signal. This work proposes the deep learning architecture that utilizes the discrete wavelet transform and the LSTM to reconstruct a generic 12-lead ECG from a reduced lead set. The experimental results are evaluated using different diagnostic measures and similarity metrics. The proposed framework is well founded, and accurate reconstruction is possible as it can capture clinically significant features and provides a robust solution against noise.

The integration of Spiking Neural Networks (SNNs) into the analysis and interpretation of physiological and speech signals has emerged as a groundbreaking approach, offering enhanced performance and deeper insights into the underlying biological processes. This review aims to summarize key advances, methodologies, and applications of SNNs within these domains, highlighting their unique ability to mimic the temporal dynamics and efficiency of the human brain. We dive into the core principles of SNNs, their neurobiological underpinnings, and the computational advantages they bring to signal processing, particularly in handling the temporal and spatial complexities inherent in physiological and speech data. Comparative analyses with conventional neural network models are presented to underscore the superior efficiency, lower power consumption, and higher temporal resolution of SNNs. The review further explores challenges and future prospects, highlighting the potential of SNNs to revolutionize wearable healthcare monitoring systems, neuroprosthetic devices, and natural language processing technologies. By providing a comprehensive overview of current strategies, this review aims to inspire innovative approaches in the field, fostering advances in real-time and energy-efficient processing of complex biological signals.

The purpose of this study is to investigate the influence of different magnetic resonance (MR) sequences on the accuracy of generating computed tomography (sCT) images for nasopharyngeal carcinoma based on CycleGAN. In this study, 143 patients' head and neck MR sequence (T1, T2, T1C, and T1DIXONC) and CT imaging data were acquired. The generator and discriminator of CycleGAN are improved to achieve the purpose of balance confrontation, and a cyclic consistent structure control domain is proposed in terms of loss function. Four different single-sequence MR images and one multi-sequence MR image were used to evaluate the accuracy of sCT. During the model testing phase, five testing scenarios were employed to further assess the mean absolute error, peak signal-to-noise ratio, structural similarity index, and root mean square error between the actual CT images and the sCT images generated by different models. T1 sequence-based sCT achieved better results in single-sequence MR-based sCT. Multi-sequence MR-based sCT achieved better results with T1 sequence-based sCT in terms of evaluation metrics. For metrological evaluation, the global gamma passage rate of sCT based on sequence MR was greater than 95% at 3%/3 mm, except for sCT based on T2 sequence MR. We developed a CycleGAN method to synthesize CT using different MR sequences, this method shows encouraging potential for dosimetric evaluation.

In this paper, a comprehensive exploration is undertaken to elucidate the utilization of Spiking Neural Networks (SNNs) within the biomedical domain. The investigation delves into the experimentally validated advantages of SNNs in comparison to alternative models like LSTM, while also critically examining the inherent limitations of SNN classifiers or algorithms. SNNs exhibit distinctive advantages that render them particularly apt for targeted applications within the biomedical field. Over time, SNNs have undergone extensive scrutiny in realms such as neuromorphic processing, Brain-Computer Interfaces (BCIs), and Disease Diagnosis. Notably, SNNs demonstrate a remarkable affinity for the processing and analysis of biomedical signals, including but not limited to electroencephalogram (EEG), electromyography (EMG), and electrocardiogram (ECG) data. This paper initiates its exploration by introducing some of the biomedical applications of EMG, such as the classification of hand gestures and motion decoding. Subsequently, the focus extends to the applications of SNNs in the analysis of EEG and ECG signals. Moreover, the paper delves into the diverse applications of SNNs in specific anatomical regions, such as the eyes and noses. In the final sections, the paper culminates with a comprehensive analysis of the field, offering insights into the advantages, disadvantages, challenges, and opportunities introduced by various SNN models in the realm of healthcare and biomedical domains. This holistic examination provides a nuanced perspective on the potential transformative impact of SNN across a spectrum of applications within the biomedical landscape.

Multiclass classification of brain tumors from magnetic resonance (MR) images is challenging due to high inter-class similarities. To this end, convolution neural networks (CNN) have been widely adopted in recent studies. However, conventional CNN architectures fail to capture the small lesion patterns of brain tumors. To tackle this issue, in this paper, we propose a global transformer network dubbed GT-Net for multiclass brain tumor classification. The GT-Net mainly comprises a global transformer module (GTM), which is introduced on the top of a backbone network. A generalized self-attention block (GSB) is proposed to capture the feature inter-dependencies not only across spatial dimension but also channel dimension, thereby facilitating the extraction of the detailed tumor lesion information while ignoring less important information. Further, multiple GSB heads are used in GTM to leverage global feature dependencies. We evaluate our GT-Net on a benchmark dataset by adopting several backbone networks, and the results demonstrate the effectiveness of GTM. Further, comparison with state-of-the-art methods validates the superiority of our model.

Practical application of surface-enhanced Raman spectroscopy (SERS) has suffered from several limitations by heterogeneous distribution of hot-spots, such as high signal fluctuation and the resulting low reliability in detection. Herein, we develop a strategy of more sensitive and reliable SERS platform through designing spatially homogeneous gold nanoparticles (GNPs) on a uniform gold nanoisland (GNI) pattern. The proposed SERS substrate is successfully fabricated by combining two non-lithographic techniques of electron beam evaporation and convective self-assembly. These bottom-up methods allow a simple, cost-effective, and large-area fabrication. Compared to the SERS substrates obtained from two separate nanofabrication methods, Raman spectra measured by the samples with both GNPs and GNIs present a significant increase in the signal intensity as well as a notable improvement in signal fluctuation. The simulated near-field analyses demonstrate the formation of highly amplified plasmon modes within and at the gaps of the GNP-GNI interfaces. Moreover, the suggested SERS sensor is evaluated to detect the glucose concentration, exhibiting that the detection sensitivity is improved by more than 10 times compared to the sample with only GNI patterns and a fairly good spatial reproducibility of 7% is accomplished. It is believed that our suggestion could provide a potential for highly sensitive, low-cost, and reliable SERS biosensing platforms that include many advantages for healthcare devices.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-024-00381-4.

Supramalleolar osteotomy (SMO) is a representative procedure to restore a malalignment in the varus ankle deformity by shifting the concentrated pressure on the medial ankle joint to the lateral area. Additionally, fibula osteotomy (FO) is selectively selected and performed according to the surgeon's preference. However, it is controversial whether FO is effective in shifting the abnormal pressure from the medial to the lateral area on the ankle joint. Some cadaveric studies have been performed to prove this. However, it is difficult to consistently reconstruct amount of the varus ankle deformities angle in cadavers and to guarantee reliable contact pressure between the ankle joint. Thus, the aim of this study was predicted and quantitatively compared a peak pressure between single SMO and SMO with FO procedure by using a finite element analysis as a powerful biomechanical tool to those limitations of cadaveric study. This study reconstructed total 4 3D foot and ankle models including a normal and pre-op model and 2 post-op models. The pre-op model was modified by assigning 10° varus tilting corresponding to stage 3b in the classification of varus ankle osteoarthritis based on the validated normal model. Also, the post-op models were reconstructed by applying single SMO and SMO with FO, respectively. All of the models were assumed as one-leg standing position and to mimic smooth ankle joint motion. Peak contact pressure change was predicted at the medial ankle joint by using computational simulation. As a result, 2 post-op models showed a remarkably peak pressure reduction by up to 5.5 times on the medial tibiotalar joint. However, a comparison between single SMO and SMO with FO model showed no appreciable differences. In conclusion, this study predicted that single SMO may be as effective as SMO with FO in reducing peak contact pressure on the medial tibiotalar joint in varus ankle osteoarthritis.

Purpose: The sacroiliac joint (SIJ), a synovial joint with irregular surfaces, is crucial for stabilizing the body and facilitating daily activities. However, recent studies have reported that 15-30% of lower back pain can be attributed to instability in the SIJ, a condition collectively referred to as sacroiliac joint dysfunction (SIJD). The aim of this study is to investigate how the morphological characteristics of the auricular surface may influence the SIJ range of motion (ROM) and to examine differences in SIJ ROM between females and males, thereby contributing to the enhancement of SIJD diagnosis and treatment.

Methods: We measured SIJ ROM using motion-analysis cameras in 24 fresh cadavers of Korean adults (13 males and 11 females). Using three-dimensional renderings of the measured auricular surface, we investigated the correlations between the morphological characteristics of the auricular surface and the ROM of the SIJ.

Results: The SIJ ROM was between 0.2° and 6.7° and was significantly greater in females (3.58° ± 1.49) compared with males (1.38° ± 1.00). Dividing the participants into high-motion (3.87° ± 1.19) and low-motion (1.13° ± 0.62) groups based on the mean ROM (2.39°) showed no significant differences in any measurements. Additionally, bone defects around the SIJ were identified using computed tomography of the high-motion group. In the low-motion group, calcification between auricular surfaces and bone bridges was observed.

Conclusion: This suggests that the SIJ ROM is influenced more by the anatomical structures around the SIJ than by the morphological characteristics of the auricular surface.

Model-based Bayesian approaches have been widely applied in Electrocardiogram (ECG) signal processing, where their performances heavily rely on the accurate selection of model parameters, particularly the state and measurement noise covariance matrices. In this study, we introduce an adaptive augmented cubature Kalman filter/smoother (CKF/CKS) for ECG processing, which updates the noise covariance matrices at each time step to accommodate diverse noise types and input signal-to-noise ratios (SNRs). Additionally, we incorporate the dynamic time warping technique to enhance the filter's efficiency in the presence of heart rate variability. Furthermore, we propose a method to significantly reduce the computational complexity required for CKF/CKS implementation in ECG processing. The denoising performance of the proposed filter was compared to those of various nonlinear Kalman-based frameworks involving the Extended Kalman filter/smoother (EKF/EKS), the unscented Kalman filter/smoother (UKF/UKS), and the ensemble Kalman filter (EnKF) that was recently proposed for ECG enhancement. In this study, we conducted a comprehensive evaluation and comparison of the performance of various nonlinear Kalman-based frameworks for ECG signal processing, which have been proposed in recent years. Our assessment was carried out on multiple normal ECG segments extracted from different entries in the MIT-BIH Normal Sinus Rhythm Database (NSRDB). This database provides a diverse set of ECG recordings, allowing us to examine the filters' denoising capabilities across various scenarios. By comparing the performance of these filters on the same dataset, we aimed to provide a thorough analysis and identification of the most effective approach for ECG denoising. Two kinds of noises were introduced to such segments: 1-stationary white Gaussian noise and 2-non-stationary real muscle artifact noise. For evaluation, four comparable measures namely the SNR improvement, PRD, correlation coefficient and MSEWPRD were employed. The findings demonstrated that the suggested algorithm outperforms the EKF/EKS, EnKF/EnKS, UKF/UKS methods in both stationary and nonstationary environments regarding SNR improvement, PRD, correlation coefficient and MSEWPRD metrics.