Computer Methods in Biomechanics and Biomedical Engineering最新文献

In the field of sleep medicine, identifying sleep-wake stages is crucial for evaluate of sleep quality. Until now, numerous methods have been proposed for sleep-wake classification. These methods predominantly utilize electroencephalogram (EEG) signals, achieving competitive performance in sleep-wake stage classification. However, acquiring EEG signals is both cumbersome and inconvenient. At the same time, EEG signals are very weak and are easily disturbed. In contrast EEG signal, collecting electrocardiogram (ECG) signals is relatively simple and convenient. Therefore, based on the ECG signals, we propose a simple and effective sleep-wake stages model that can be used for wearable devices. In order to extract multi-scale features of ECG signals, convolutional kernels of different sizes are designed. Then, a novel dynamic connection convolutional neural network (DCCNN) is proposed to classify sleep-wake stages. First, the DCCNN calculates the goodness of feature maps from each layer. Second, according to the goodness of different layers, select the optimal layer to form a residual module with the current layer. The proposed method was tested on sleep data from a publicly accessible databases, namely the MIT-BIH Polysomnographic Database, resulting in an best accuracy of 92.21%. The findings are similar and higher performance to those models trained with EEG signals. Moreover, when compared to state-of-the-art methods, the proposed approach's effectiveness is further demonstrated. In conclusion, this research offers a novel approach for sleep monitoring.

Electroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2% accuracy, outperforming baseline methods. This framework advances EEG based speech recognition aiding brain-computer interfaces and assistive technologies for individuals with speech disorders.

Diabetes is a chronic condition that affects blood sugar levels and vital organs in the body. Early detection is crucial given the increasing global prevalence of diabetes and the grave risk of complications if not properly managed. Thus, a good prediction system is necessary. Although the Decision Tree (DT) is commonly used for classification, it is less effective with large datasets. We propose hyperparameter optimization of the DT using the Grey Wolf Optimization (GWO), which has exploration and both exploitation capabilities. However, the limited search space of GWO may hinder practical exploration and exploitation, leading to premature optimization. To address this, we propose a modified GWO (MGWO) by adding the Levy distribution function to enhance the movements of alpha, beta, and delta wolves. We also provide GA (Genetic Algorithm) as a comparative algorithm. The fitness value of MGWO is 0.8498, surpassing GWO (0.8373) and GA (0.8492). Evaluation results indicate that MGWO and GA yield similar and superior accuracy compared to GWO. The proposed method outperforms existing ones. Further research is needed to evaluate the impact of varying the number of wolves on optimization performance and classification accuracy.

Attention management is crucial for cognitive development, especially in children. This study presents a novel brain-computer interface (BCI) system that uses EEG signals to classify attention states. It analyzes these signals using a waveform ratio feature extraction method and visualizes attention levels through a drone's altitude. The system provides real-time feedback via a GUI and incorporates gamified elements like drone control to enhance engagement and training efficacy. Experimental results show that positive response mechanisms significantly improve focus and motivation, demonstrating the system's potential to transform traditional attention training methods.

This paper presents a basic model for the therapeutic effect of two drugs on patients with early cervical cancer. Two cases are considered: with constant control and with optimal control. For case one, the system is proved to have a unique non-negativity solution if the initial values are positive; then the sufficient conditions for the existence and stability of the equilibriums are derived; and Hopf bifurcation is also considered. For case two, by using the Pontryagin's Maximum Principle, we get the optimal control solution. Some examples and numerical simulations are presented. Discussions and conclusions are listed at the end.

Considering how scientific literature on the subject has been enriched with increasingly significant contributions in both sociological and organizational fields, this short essay aims to define how the use of artificial intelligence can define a valuable compensatory tool aimed at promoting greater inclusiveness in daily life activities for those in neurological deficit. In particular, we intend to focus on the benefits of sports activity in terms of increased physical and mental well-being through the use of artificial intelligence and sensors.

Cardiovascular disease is a leading cause of mortality, necessitating early and precise prediction for improved patient outcomes. This study proposes Quantum-HeartDiseaseNet, a novel heart disease risk prediction framework that integrates a Dynamic Opposite Pufferfish Optimization Algorithm for feature selection and a Quantum Attention-based Bidirectional Gated Recurrent Unit (QABiGRU) for accurate diagnosis. The feature selection method enhances diagnosis accuracy while reducing dimensionality, and Synthetic Minority Oversampling Technique (SMOTE) addresses data imbalance. Evaluated on three heart disease datasets, the proposed model achieved 98.87% accuracy, 98.74% precision, and 98.56% recall, outperforming conventional methods. Experimental results validate its effectiveness in early disease prediction.

The biomechanical properties of the lumbar pedicle screw technique, cortical bone trajectory screw technique, and modified cortical bone trajectory screw technique when used individually in internal fixation systems and their effects on neighboring segments have been studied. However, there are fewer studies on the combined use of the modified cortical bone trajectory screw technique and the cortical bone trajectory screw technique. This paper focuses on analyzing the stresses in the internal fixation system when the modified and traditional cortical bone trajectory screw techniques are applied jointly, and the effects on the adjacent segments using the finite element analysis.

The Oxford Foot Model (OFM) is a widely-used multi-segment foot model for the evaluation of foot motion. To date, custom code based on the original scientific publications have failed to reproduce results available through the Vicon plug-in (ViconOFM). This highlights a lack of transparency, affecting the accessibility and understanding of the model. Therefore, the aims of this study are to (1) replicate ViconOFM using Python for open-source distribution (openOFM v1.0) and (2) reproduce the original scientific description of the OFM in a second version (openOFM v1.1), highlighting differences between both versions. A dataset comprising one healthy adult and a set of five patients with heterogeneous foot pathologies was used for analyses. Evaluation was conducted using the normalised root mean square error (NRMSE) between the inter-segment angles and arch heights of both implementations. The openOFM v1.1 was developed based on the original OFM publications. The average NRMSE between ViconOFM and openOFM v1.0, using both healthy and pathological gait, was of 0.0012. Based on our openOFM v1.1 implementation, differences between ViconOFM and the original OFM description from the literature are due to an integrated smoothing and gap filling function and changes in segment definitions. The negligible differences between ViconOFM and openOFM v1.0 in healthy and pathological gait supports the concurrent validity of openOFM. Providing users with both openOFM versions enables informed use of either model and allows further investigation into the implications of these differences. The open-source nature of the project promotes further development.

Contemporary neuroscience scientists are interested in dyslexia, a complicated brain neurodevelopmental disorder. This condition causes slow and imprecise word comprehension in 5%-17% of the global population across languages and cultures. People with dyslexia often discuss mental health. On the scalp, the EEG signal shows coordinated neural activity that synchronizes. The EEG signal accurately captures these cerebral activity fluctuations due to evolution and mental state. Using statistical approaches, this study will determine if EEG waves indicate sickness. For this, three measures are suggested. The first metric, power spectral density, shows signal frequency and power distribution. The second metric assesses the model's uncertainty or randomness, conveying signal information, using entropy. The third metric, the Kolmogorov-Smirnov Test, uses entropy-based measurements to identify distributions based on Kolmogorov complexity. Applying these measures to the overall EEG signal of the twenty students under study separated the seven students' information from the other thirteen.