Notes

Soreness treatment values mediate their bond among pain catastrophizing and opioid health professional prescribed utilization in people using long-term non-cancer pain.
The sleep spindles in EEG have become one type of biomarker used to assess cognitive abilities and related disorders, and thus their detection is crucial for clinical research. This task, traditionally performed by sleep experts, is time-consuming. Many methods have been proposed to automate this process, yet an increase in performance is still expected. Inspired by the application in image segmentation, we propose a point-wise spindle detection method based on the U-Net framework with an attention module (SpindleU-Net). It maps the sequences of arbitrary-length EEG inputs to those of dense labels of spindle or non-spindle on freely chosen intervals. The attention module that focuses on the salient spindle region allows better performance, and a task-specific loss function is defined to alleviate the problem of imbalanced classification. As a deep learning method, SpindleU-Net outperforms state-of-the-art methods on the widely used benchmark dataset of MASS as well as the DREAMS dataset with a small number of samples. On MASS dataset it achieves average F1 scores of 0.854 and 0.803 according to its consistency with the annotations by two sleep experts respectively. On DREAMS dataset, it shows the average F1 score of 0.739. Its cross-dataset performance is also better compared to other methods, showing the good generalization ability for cross-dataset applications.Mental disorders are a major source of disability, with few effective treatments. It has recently been argued that these diseases might be effectively treated by focusing on decision-making, and specifically remediating decision-making deficits that act as "ingredients" in these disorders. Prior work showed that direct electrical brain stimulation can enhance human cognitive control, and consequently decision-making. This raises a challenge of detecting cognitive control lapses directly from electrical brain activity. Here, we demonstrate approaches to overcome that challenge. We propose a novel method, referred to as maximal variance node merging (MVNM), that merges nodes within a brain region to construct informative inter-region brain networks. We employ this method to estimate functional (correlational) and effective (causal) networks using local field potentials (LFP) during a cognitive behavioral task. The effective networks computed using convergent cross mapping differentiate task engagement from background neural activity with 85% median classification accuracy. We also derive task engagement networks (TENs) networks that constitute the most discriminative inter-region connections. Subsequent graph analysis illustrates the crucial role of the dorsolateral prefrontal cortex (dlPFC) in task engagement, consistent with a widely accepted model for cognition. We also show that task engagement is linked to prefrontal cortex theta (4-8 Hz) oscillations. We, therefore, identify objective biomarkers associated with task engagement. These approaches may generalize to other cognitive functions, forming the basis of a network-based approach to detecting and rectifying decision deficits.Cochlear implants are very well established in the rehabilitation of hearing loss and are regarded as the most successful neuroprostheses to date. While a lot of progress has also been made in the neighboring field of specific vestibular implants, some diseases affect the entire inner ear, leading to both hearing and vestibular hypo- or dysfunction. The proximity of the cochlear and vestibular organs suggests a single combined implant as a means to alleviate the associated impairments. While both organs can be stimulated in a similar way with electric pulses applied through implanted electrodes, the typical phase durations needed in the vestibular system seem to be substantially larger than those typically needed in the cochlear system. Therefore, when using sequential stimulation in a combined implant, the pulse stream to the cochlea is interrupted by comparatively large gaps in which vestibular stimulation can occur. We investigate the impact of these gaps in the auditory stream on speech perception. Specifically, we compare a number of stimulation strategies with different gap lengths and distributions and evaluate whether it is feasible to use them without having a noticeable decline in perception and quality of speech. This is a prerequisite for any practicable stimulation strategy of a combined system and can be investigated even in recipients of a normal cochlear implant. Our results show that there is no significant deterioration in speech perception for the different strategies examined in this paper, leaving the strategies as viable candidates for prospective combined cochleo-vestibular implants.Tripping is accompanied by reduced minimum toe clearance (mTC) during the swing phase of gait. The risk of fall due to tripping among transfemoral amputees is nearly 67% which is greater than the transtibial amputees. Therefore, intervention to improve mTC can potentially enhance the quality of life among transfemoral amputees. In this paper, we first develop a real-time visual feedback system with center of pressure (CoP) information. Next, we recruited six non-disabled and three transfemoral amputees to investigate the effect on mTC while participants were trained to shift the CoP anteriorly/posteriorly during heel strike. Finally, to assess the lasting effect of training on mTC, retention trials were conducted without feedback. Cirtuvivint cost During feedback, posterior shift in the CoP improved the mTC significantly from 4.68 ± 0.40 cm to 6.12 ± 0.68 cm (p 0.05) from feedback condition in amputee, suggesting a positive effect of feedback training. The foot-to-ground angle (FGA) at mTC increased significantly (p less then 0.025) during posterior shift feedback in non-disabled suggests active ankle dorsiflexion in increasing mTC. However, in amputees, FGA at mTC did not differ significantly during both anterior and posterior CoP shift feedback. The present findings suggest CoP feedback as a potential strategy during gait rehabilitation of transfemoral amputees.
Wearable devices have created new opportunities in healthcare and sport sciences by unobtrusively monitoring physiological signals. Textile polymer-based electrodes proved to be effective in detecting electrophysiological potentials but suffer mechanical fragility and low stretch resistance. The goal of this research is to develop and validate in dynamic conditions cost-effective and easily manufacturable electrodes characterized by adequate robustness and signal quality.

We here propose an optimized screen printing technique for the fabrication of PEDOTPSS-based textile electrodes directly into finished stretchable garments for surface electromyography (sEMG) applications. A sensorised stretchable leg sleeve was developed, targeting five muscles of interest in rehabilitation and sport science. An experimental validation was performed to assess the accuracy of signal detection during dynamic exercises, including sit-to-stand, leg extension, calf raise, walking, and cycling.

The electrodes can resist up tection of sEMG in dynamic conditions is necessary.Steady-state visual evoked potential (SSVEP) is widely used in electroencephalogram (EEG) control, medical detection, cognitive neuroscience, and other fields. However, successful application requires improving the detection performance of SSVEP signal frequency characteristics. Most strategies to enhance the signal-to-noise ratio of SSVEP utilize application of a spatial filter. Here, we propose a method for image filtering denoising (IFD) of the SSVEP signal from the perspective of image analysis, as a preprocessing step for signal analysis. Arithmetic mean, geometric mean, Gaussian, and non-local means filtering methods were tested, and the experimental results show that image filtering of SSVEP cannot effectively remove the noise. Thus, we proposed a reverse solution in which the SSVEP noise signal was obtained by image filtering, and then the noise was subtracted from the original signal. Comparison of the recognition accuracy of the SSVEP signal before and after denoising was used to evaluate the denoising performance for stimuli of different duration. After IFD processing, SSVEP exhibited higher recognition accuracy, indicating the effectiveness of this proposed denoising method. Application of this method improves the detection performance of SSVEP signal frequency characteristics, combines image processing and brain signal analysis, and expands the research scope of brain signal analysis for widespread application.RadViz contributes to multidimensional analysis by using 2D points for encoding data elements and allowing for interpreting them along the original data dimensions. For these characteristics it is used in different application domains, like clustering, anomaly detection, and software visualization. However, it is likely that using the dimension arrangement that comes with the data will produce a plot which leads users to make inaccurate conclusions about points values and data distribution. This paper attacks this problem without altering the original RadViz design it defines, for both a single point and a set of points, the metric of effectiveness error, and uses it to define the objective function of a dimension arrangement strategy, arguing that minimizing it increases the overall RadViz visual quality. This paper investigated the intuition that reducing the effectiveness error is beneficial also for other well-known RadViz problems, like points clumping toward the center, many-to-one plotting of non-proportional points, and cluster separation. It presents an algorithm that reduces to zero the effectiveness error for a single point and a heuristic that approximates the dimension arrangement minimizing the effectiveness error for an arbitrary set of points. A set of experiments based on 21 real datasets has been performed, with the goals of analyzing the advantages of reducing the effectiveness error, comparing the proposed dimension arrangement strategy with other related proposals, and investigating the heuristic accuracy. The Effectiveness Error metric, the algorithm, and the heuristic presented in this paper have been made available in a d3.js plugin at https//aware-diag-sapienza.github.io/d3-radviz.Shear-wave elastography (SWE) measures shear-wave speed (SWS), which is related to the underlying shear modulus of soft tissue. SWS in soft tissue changes depending on the amount of the external strain that soft tissue is subjected to due to the acoustoelastic (AE) phenomenon. In the literature, variations of SWS as a function of applied uniaxial strain were used for nonlinear characterization, assuming soft tissues to be elastic, although soft tissues are indeed viscoelastic in nature. Hence, nonlinear characterization using SWS alone is insufficient. In this work, we use SWS together with shear-wave attenuation (SWA) during incremental quasi-static compressions in order to derive biomechanical characterization based on the AE theory in terms of well-defined storage and loss moduli. As part of this study, we also quantify the effect of applied strain on measurements of SWS and SWA, since such confounding effects need to be taken into account when using SWS and/or SWA, e.g., for staging a disease state, while such effects can also serve as an additional imaging biomarker.
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