Biomedical Engineering / Biomedizinische Technik最新文献

Surface Electromyographic (sEMG) signal is a prime source of information to activate prosthetic hand such that it is able to restore a few basic hand actions of amputee, making it suitable for rehabilitation. In this work, a non-invasive single channel sEMG amplifier is developed that captures sEMG signal for three typical hand actions from the lower elbow muscles of able bodied subjects and amputees. The recorded sEMG signal detrends and has frequencies other than active frequencies. The Empirical Mode Decomposition Detrending Fluctuation Analysis (EMD-DFA) is attempted to de-noise the sEMG signal. A feature vector is formed by extracting eight features in time domain, seven features each in spectral and wavelet domain. Prominent features are selected by Fuzzy Entropy Measure (FEM) to ease the computational complexity and reduce the recognition time of classification. Classification of different hand actions is attempted based on multi-class approach namely Partial Least Squares Discriminant Analysis (PLS-DA) to control the prosthetic hand. It is inferred that an accuracy of 89.72% & 84% is observed for the pointing action whereas the accuracy for closed fist is 81.2% & 79.54% while for spherical grasp it is 80.6% & 76% respectively for normal subjects and amputees. The performance of the classifier is compared with Linear Discriminant Analysis (LDA) and an improvement of 5% in mean accuracy is observed for both normal subjects and amputees. The mean accuracy for all the three different hand actions is significantly high (83.84% & 80.18%) when compared with LDA. The proposed work frame provides a fair mean accuracy in classifying the hand actions of amputees. This methodology thus appears to be useful in actuating the prosthetic hand.

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection have some limitations, and it is essential to find an objective and efficient method. With the development of automation and artificial intelligence, computer-aided diagnosis has attracted more and more attention. Therefore, exploring the use of deep learning (DL) to detect depression has valuable potential. In this paper, convolutional neural network (CNN) is applied to build a diagnostic model for depression based on electroencephalogram (EEG). EEG recordings are analyzed by three different CNN structures, namely EEGNet, DeepConvNet and ShallowConvNet, to dichotomize depression patients and healthy controls. EEG data were collected in the resting state from three electrodes (Fp1, Fz, Fp2) among 80 subjects (40 depressive patients and 40 normal subjects). After the preprocessing step, the DL structures are employed to classify the data, and their recognition performance is evaluated by comparing the classification results. The classification performance shows that depression was effectively detected using EEGNet with 93.74% accuracy, 94.85% sensitivity and 92.61% specificity. In the process of optimizing the parameters of EEGNet structure, the highest accuracy can reach 94.27%. Compared with traditional diagnostic methods, EEGNet is highly worthy for the future depression detection and valuable in terms of accuracy and speed.

The macro and micro design is essential to the biomechanical performance of a short implant. In this study, the implant thread parameters of short implants used in edentulous maxillae will be discussed. The aim of the study is to analyse biomechanical distinctions in different thread parameters over short implants by applying the vertical or oblique load of 130 N on dental prosthesis. A 6*5 mm implant will be used in posterior maxillae arch, where the molar region locates. The CAD model has been assembled by three parts, a crown, an implant system and a jaw. By applying the vertical or oblique load to the crown, the Von-Mises stresses of cortical bone and trabecular bone will be evaluated in pairs along the lines v1-v2 & a1-a2. The results showed that the reverse buttress thread would induce more stresses in cancellous bone whereas the buttress did the opposite. The trapezoidal thread (V-thread) is more favourable than the reverse buttress thread in accordance to the FEA result. The rectangle threads will induce more uneven stresses in cancellous bones.

Decoding the cellular network interaction of neurons and glial cells are important in the development of new therapies for diseases of the central nervous system (CNS). Electrophysiological in vivo studies in mice will help to understand the highly complex network. In this paper, the optimization of epidural liquid crystal polymer (LCP) electrodes for different platinum electroplating parameters are presented and compared. Constant current and pulsed current electroplating varied in strength and duration was used to decrease the electrode impedance and to increase the charge storage capacity (CSC_C). In best cases, both methods generated similar results with an impedance reduction of about 99%. However, electroplating with pulsed currents was less parameter-dependent than the electroplating with constant current. The use of ultrasound was essential to generate platinum coatings without plating defects. Electrode model parameters extracted from the electrode impedance reflected the increase in surface porosity due to the electroplating processes.

This study has the aim to investigate the strain and stress in an anterolateral locking plate applied for the fixation of a lateral split fracture. To simulate a complex fracture situation, three segments were separated. With a FEM analysis, representative places for strain and stress measurement were determined. A locked osteosynthesis plate was instrumented with strain gauges and tested on a fractured and a non-fractured Saw Bone model. To simulate different loading situations, four different points of force application, from the center of the condyles to a 15 mm posterior position, were used with a medial-lateral load distribution of 60:40. The simulations as well as the biomechanical tests demonstrated that two deformations dominate the load on the plate: a bending into posterior direction and a bulging of the plate head. Shifting the point of application to the posterior direction resulted in increasing maximum stress, from 1.16 to 6.32 MPa (FEM analysis) and from 3.04 to 7.00 MPa (biomechanical study), respectively. Furthermore, the comparison of the non-fractured and fractured models showed an increase in maximum stress by the factor 2.06-2.2 (biomechanical investigation) and 1.5-3.3 (FEM analysis), respectively.

Image quality (IQ) assessment plays an important role in the medical world. New methods to evaluate image quality have been developed, but their application in the context of computer tomography is yet limited. In this paper the performance of fifteen well-known full reference (FR) IQ metrics is compared with human judgment using images affected by metal artifacts and processed with metal artifact reduction methods from a phantom. Five region of interest with different sizes were selected. IQ was evaluated by seven experienced radiologists completely blinded to the information. To measure the correlation between FR-IQ, and the score assigned by radiologists non-parametric Spearman rank-order correlation coefficient and Kendall's Rank-order Correlation coefficient were used; so as root mean square error and the mean absolute error to measure the prediction accuracy. Cohen's kappa was employed with the purpose of assessing inter-observer agreement. The metrics GMSD, IWMSE, IWPSNR, WSNR and OSS-PSNR were the best ranked. Inter-observer agreement was between 0.596 and 0.954, with p<0.001 in all study. The objective scores predicted by these methods correlate consistently with the subjective evaluations. The application of this metrics will make possible a better evaluation of metal artifact reduction algorithms in future works.

The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

Surgeons lose most of their tactile tissue information during minimal invasive surgery and need an additional tool of intraoperative tissue recognition. Confocal laser microscopy (CLM) is a well-established method of tissue investigation. The objective of this study was to analyze the feasibility and diagnostic accuracy of CLM nervous tissue recognition. Images taken with an endoscopic CLM system of sympathetic ganglions, nerve fibers and pleural tissue were characterized in terms of specific signal-patterns ex-vivo. No fluorescent dye was used. Diagnostic accuracy of tissue classification was evaluated by newly trained observers (sensitivity, specificity, PPV, NPV and interobserver variability). Although CLM images showed low CLM image contrast, assessment of nerve tissue was feasible without any fluorescent dye. Sensitivity and specificity ranged between 0.73 and 0.9 and 0.55-1.0, respectively. PPVs were 0.71-1.0 and the NPV range was between 0.58 and 0.86. The overall interobserver variability was 0.36. The eCLM enables to evaluate nervous tissue and to distinguish between nerve fibers, ganglions and pleural tissue based on backscattered light. However, the low image contrast and the heterogeneity in correct tissue diagnosis and a fair interobserver variability indicate the limit of CLM imaging without any fluorescent dye.