Computer Methods in Biomechanics and Biomedical Engineering最新文献

Recent reports have highlighted a notable prevalence of atypical hangman's fractures, yet their biomechanical aspects remain underexplored. Using a validated finite element model, this study assesses changes in rotation-moment characteristics of the upper cervical spine due to fractures involving the superior and inferior articular process, pars interarticularis, and lamina. The results revealed that fractures affecting the superior articular process and pars interarticularis led to significant instability, particularly in axial rotation and extension. However, atypical hangman's fractures did not necessarily produce greater instability than Levine-Edwards type II hangman's fractures.

Clinical implications: The present data contribute to the specialties of Sports Dentistry and Implantology, offering scientific evidence of the importance of a mouthguard to provide the best protection for athletes rehabilitated with dental implants.

We introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose-effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets.

Currently, an important challenge in stroke rehabilitation is how to effectively restore motor functions of lower limbs. This paper presents multimodal human computer interaction (HCI) of wheelchairs supporting lower limb active rehabilitation. First, multimodal HCI incorporating motor imagery electroencephalography (EEG), electromyography (EMG) and speech is designed. Second, prototype supporting wheelchair active rehabilitation method is illustrated in details. Third, the preliminary brain-computer interfaces (BCI) and speech recognition task experiments are carried out respectively, and the results are obtained. Finally, discussion is conducted and conclusion is drawn. This study has important practical significance in auxiliary movements and neurorehabilitation for stroke patients.

Electroencephalogram (EEG) signals, which objectively reflect the state of the brain, are widely favored in emotion recognition research. However, the presence of cross-session and cross-subject variation in EEG signals has hindered the practical implementation of EEG-based emotion recognition technologies. In this article, we propose a multi-source domain transfer method based on subdomain adaptation and minimum class confusion (MS-SAMCC) in response to the addressed issue. First, we introduce the mix-up data augmentation technique to generate augmented samples. Next, we propose a minimum class confusion subdomain adaptation method (MCCSA) as a sub-module of the multi-source domain adaptation module. This approach enables global alignment between each source domain and the target domain, while also achieving alignment among individual subdomains within them. Additionally, we employ minimum class confusion (MCC) as a regularizer for this sub-module. We performed experiments on SEED, SEED IV, and FACED datasets. In the cross-subject experiments, our method achieved mean classification accuracies of 87.14% on SEED, 63.24% on SEED IV, and 42.07% on FACED. In the cross-session experiments, our approach obtained average classification accuracies of 94.20% on SEED and 71.66% on SEED IV. These results demonstrate that the MS-SAMCC approach proposed in this study can effectively address EEG-based emotion recognition tasks.

The application of balloon dilation catheters in the management of intracranial arterial stenosis has been gradually increasing. However, studies on the feasibility and effectiveness of different types of balloons remain relatively scarce. In this study, catheter models of three different materials were designed to simulate balloon crimping,splitting, and dilatation processes. A compliant balloon produces large deformations with poor dilatation and a stress concentration phenomenon. During dilatation, the shear stress generated in the intima and lesion area by the semi-compliant balloon was smaller than that generated by the non-compliant balloon. These results demonstrate the feasibility of using semi-compatible balloons.

Post-stroke Dysarthria (PSD) is one of the common sequelae of stroke. PSD can harm patients' quality of life and, in severe cases, be life-threatening. Most of the existing methods use frequency domain features to recognize the pathological voice, which makes it hard to completely represent the characteristics of pathological voice. Although some results have been achieved, there is still a long way to go for practical applications. Therefore, an improved deep learning-based model is proposed to classify between the pathological voice and the normal voice, using a novel fusion feature (MSA) and an improved 1D ResNet network hybrid bi-directional LSTM with dilated convolution (named 1D DRN-biLSTM). The experimental results show that our fusion features bring greater improvement in pathological speech recognition than the method that only analyzes the MFCC features, and can better synthesize the hidden features that characterize pathological speech. In terms of model structure, the introduction of dilated convolution and LSTM can further improve the performance of the 1D Resnet network, compared to ordinary networks such as CNN and LSTM. The accuracy of this method reaches 82.41% and 100% at the syllable level and speaker level, respectively. Our scheme outperforms other existing methods in terms of feature learning capability and recognition rate, and will help to play an important role in the assessment and diagnosis of PSD in China.

Adult-acquired flatfoot causes various deformities. If a patient-specific foot model can be created using the finite element method, it can be used to study the appropriate surgical technique for each patient. Nine patient-specific flatfoot models were created, and loading simulations were performed. To validate the models, the patients' weight-bearing radiographs were compared with the parameters of the models. The CCC values ranged from 0.917 to 0.993 , all exceeding the moderate threshold according to the McBride criteria. Our model reproduces the biomechanics of a patient's foot under loading conditions, which may be useful for investigating patient-specific surgical procedures.

Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.

This study proposes a novel model employing nonlinear ordinary differential equations to dissect HCV dynamics. Six distinct population groups are delineated: Susceptible, Treatment, Responder, Non-Responder, Cured, and Fibrosis. A detailed numerical analysis of this model was conducted, tracking the predicted trends over a span of 20 years. The primary objective of this analysis is to assess and confirm the model's predictive accuracy and its potential to supplant invasive diagnostic methods in monitoring the progression of liver fibrosis. By incorporating various control parameters, namely $u_1\left(t\right),u_2\left(t\right),$ and $u_3\left(t\right),$ the model offers a nuanced perspective on disease progression and treatment outcomes. Parameter $u_1\left(t\right)$ modulates treatment-induced fibrosis progression, providing a crucial lever for mitigating treatment-related side effects. $u_2\left(t\right)$ reflects treatment effectiveness, capturing the proportion of responders within the treatment cohort. Meanwhile, $u_3\left(t\right)$ governs fibrosis progression in non-responders, shedding light on the disease's natural trajectory without effective treatment.