Computer Methods in Biomechanics and Biomedical Engineering最新文献

This study proposes a method for analysing decomposed electromyographic signals to enhance the characterization of motor units (MU). It involves two steps: (i) clustering groups of motor units based on firing rate (FR), recruitment threshold, and MU action potential amplitude, and (ii) segmentation of data at the time of interest. Such method, capable of distinguishing different groups of MU, could help understanding muscle force production. FR of MU in Vastus Lateralis and Rectus Femoris during knee extension was investigated. The findings distinguish MU groups and reveals differences in: FR between both contractions types; clustered groups; and segmented MU data in both contraction types.

Different gravity fields have important effects on the structural morphology of bone. The fluid flow caused by loadings in the bone lacunar-canalicular system (LCS), converts mechanical signals into biological signals and regulates bone reconstruction by affecting effector cells, which ensures the efficient transport of signaling molecules, nutrients, and waste products. In this study, the fluid flow and mass transfer effects of bone lacunar-canalicular system at multi-scale were firstly investigated, and a three-dimensional axisymmetric fluid-solid coupled finite element model of the LCS within three continuous osteocytes was established. The changes in fluid pressure field, flow velocity field, and fluid shear force variation on the surface of osteocytes within the LCS were studied comparatively under different gravitational fields (0 G, 1 G, 5 G), frequencies (1 Hz, 1.5 Hz, 2 Hz) and forms of cyclic compressive loading. The results showed that different frequencies represented different exercise intensities, suggesting that high-intensity exercise may accelerate the fluid flow rate within the LCS and enhance osteocytes activity. Hypergravity enhanced the transport of solute molecules, nutrients, and signaling molecules within the LCS. Conversely, the mass transfer in the LCS may be inhibited under microgravity, which may cause bone loss and eventually lead to the onset of osteoporosis. This investigation provides theoretical guidance for rehabilitative training against osteoporosis.

This study introduces novel deep learning (DL) techniques for effective fitness prediction using a person's health data. Initially, pre-processing is performed in which data cleaning, one-hot encoding and data normalization are performed. The pre-processed data are then fed into the feature selection stage, where the useful features are extracted using the enhanced chameleon swarm (ECham-Sw) optimization technique. Then, a clustering process is performed using Minkowski integrated gravity center clustering (Min-GCC) to cluster the health profiles of each individual. Finally, the Pyramid Dilated EfficientNet-B3 (PyDi-EfficientNet-B3) technique is proposed to predict the fitness of each individual efficiently with enhanced accuracy of 99.8%.

Diabetes Mellitus (DM) is the most hazardous public health challenge requiring engineering study to prevent disease complications. In this paper, a Sorensen-based diabetic model is presented in which the insulin-glucose process of a Type 1 patient is maintained by considering other factors such as physical characteristics and changes in mental aspects of the diabetic patient. The purpose of the research is to include a non-linear model of a patient with diabetes who is affected by stress, meals, exercise, and Insulin Sensitivity (IS), and a suitable RECCo controller is designed as a notable recent innovation that implements the concept of ANYA fuzzy rule-based system, which is an online adaptive type of controller that is used in this research work with an uncertainty case of the condition, where the blood glucose must be regulated. To ensure the performance of the proposed controller, a simple insulin pump is designed in a practical case, and a hardware experiment is conducted. The result of the hardware is analyzed and shows the success of the implementation of the controller in blood glucose regulation, thereby preventing complications such as hypoglycemia and hyperglycemia. The comparison analysis of RECCo was performed with other types of controllers, such as MPC and MRAC. The accuracy of the model was validated using the N-BEATS algorithm with a data-set collected from the simulated model, which is around 98%.

This article proposes a noninvasive computer-aided assessment approach based on optimized convolutional neural network for healthy and pathological voice detection. Firstly, the input voice samples are first transformed into mel-spectrogram time-frequency visual representations and fed for training the CNN model. The time-frequency image captures inherent speech variations beneficial for healthy and pathological voice sample detection. The weights and biases of trained CNN network are further optimized using artificial bee colony (ABC) optimization algorithm resulting in optimum CNN network employed for testing unseen data. The proposed approach is evaluated using three popular and publicly available datasets: SVD, AVPD and VOICED. Experimental results emphasize that proposed ABC optimized CNN model shows improved accuracy performance by 1.02% compared to conventional CNN network illustrating data-independent discriminative representation ability. Finally, gradient-weighted class activation mapping (Grad-CAM) explainable artificial intelligence (XAI) is utilized to make the decision understandable.

The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence.

In this research article, a spiking neural network-based simulation of the hippocampus is performed to model the functionalities of episodic memory. The purpose of the simulation is to find a computational model through the biological architecture of the hippocampus and correct values for their architectural biological parameters to support the episodic memory functionalities. The episodic store of the model is represented by the collection of events, where each event is further subdivided into coactive activities of experience. The model has tried to mimic the three functionalities of episodic memory, which are pattern separation, pattern association, and their recallings. In pattern separation model used the dentate biological connectivity to generate almost different output patterns corresponding to similar input patterns to reduce interference between two similar memory traces so that ambiguity can be reduced during recalling. In pattern association, an STDP based event encoding and forgetting mechanism are used to mimic the encoding function of the CA3 region in which the coactive activities get associated with each other. A decoder is proposed based on CA1, which can answer the stored event related queries. Along with these functionalities model also supports recalling and encoding based forgetting. Experimental work is performed on the model for the given set of events to check for the pattern separation efficiency, pattern completion efficiency and to check the capability of decoding the answer. An empirical analysis of the results is done and compared with the SMRITI model of episodic memory.

Electrocardiogram (ECG) is a critical biomedical signal and plays an imperative role in diagnosing cardiovascular disorders. During ECG data acquisition in clinical environment, noise is frequently present. Various noises such as powerline interference (PLI) and baseline wandering (BLW) distort the ECG signal which may lead to incorrect interpretation. Consequently, substantial emphasis has been dedicated to ECG denoising for reliable diagnosis and analysis. In this study, a novel hybrid ECG denoising method based on variational mode decomposition (VMD) and the empirical wavelet transform (EWT) is presented. For effective denoising using the VMD and EWT approach, the noisy ECG signal is decomposed within narrow-band variational mode functions (VMFs). The aim is to remove noise from these narrow-band VMFs. In current approach, the centre frequency of each VMF was computed and utilized to design an adaptive wavelet filter bank using EWT. This leads to effective removal of noise components from the signal. The proposed approach was applied to ECG signals obtained from the MIT-BIH Arrhythmia database. To evaluate the denoising performance, noise sources from the MIT-BIH Noise Stress Test Database (NSTDB) are used for simulation. The assessment of denoising performance in based on two key metrics: the percentage-root-mean-square difference (PRD) and the signal-to-noise ratio (SNR). The findings of the simulation experiment demonstrate that the suggested method has lower percentage root mean square difference and higher signal-to-noise ratio as compared to existing state of the art denoising methods. An average output SNR of 24.03 was achieved, along with a 5% reduction in PRD.

Tissues' nearly incompressibility was well reported in the literature but little effort has been made to compare volume variations computed by simulations with in vivo measurements. In this study, volume changes of the fat pad during controlled indentations of the human heel region were estimated from segmented medical images using digital volume correlation. The experiment was reproduced using finite element modelling with several values of Poisson's ratio for the fat pad, from 0.4500 to 0.4999. A single value of Poisson's ratio could not fit all the indentation cases. Estimated volume changes were between 0.9% - 11.7%.

An improved DeepSurv model is proposed for predicting the prognosis of colorectal cancer patients at stage IV. Our model, called as PseudoDeepSurv, is optimized by a novel loss function, which is the combination of the average negative log partial likelihood and the mean-squared error derived from the pseudo-observations approach. The public BioStudies dataset including 999 patients was utilized for performance evaluation. Our PseudoDeepSurv model produced a C-index of 0.684 and 0.633 on the training and testing dataset, respectively. While for the original DeepSurv model, the corresponding values are 0.671 and 0.618, respectively.