Objective. To investigate the effect of neuromuscular electrical stimulation (NMES) combined with repetitive transcranial magnetic stimulation (rTMS) on upper limb motor dysfunction in stroke patients with hemiplegia. Methods. A total of 240 stroke patients with hemiplegia who met the inclusion criteria were selected and randomly divided into 4 groups (60 cases in each group): control group, NMES group, rTMS group, and NMES + rTMS group. Before treatment and 4 weeks after treatment, we evaluated and compared the results including Fugl-Meyer assessment of upper extremity (FMA-UE) motor function, modified Barthel index (MBI), modified Ashworth scale (MAS), and motor nerve electrophysiological results among the 4 groups. Results. Before treatment, there was no significant difference in the scores of FMA-UE, MBI, MAS, and motor nerve electrophysiological indexes among the four groups, with comparability. Compared with those before treatment, the scores of the four groups were significantly increased and improved after treatment. And the score of the NMES + rTMS group was notably higher than those in the other three groups. Conclusion. NMES combined with rTMS can conspicuously improve the upper extremity motor function and activities of daily life of stroke patients with hemiplegia, which is worthy of clinical application and promotion.
As an important class of non-Gaussian statistic model, α-stable distribution has received much attention because of its generality to represent impulsive interference. Unfortunately, it does not have a closed-form probability density function (PDF) except for a few cases. For this reason, suboptimal zero-memory non-linearity (ZMNL) function has to be used as an approximation in designing locally optimal detector, such as classical Cauchy and Gaussian-tailed ZMNL (GZMNL). To enhance the performance of detectors, the authors first investigate the approximate PDFs for the symmetric α-stable. In particular, a simplified version of Cauchy-Gaussian mixture (CGM) model, called bi-parameter CGM (BCGM) model is detailed. This BCGM model has a concise closed-form, and hence is more tractable than the classical Gaussian mixture model and CGM model. Then based on the preset false alarm ratio (FAR), the test threshold is adaptively evaluated by using BCGM to maintain a constant FAR. The authors further devise an algebraic-tailed ZMNL (AZMNL) with a simplified form. Simulation results show that the detector with AZMNL outperforms the ones with classical Cauchy and GZMNL, and achieves near-optimal performance in varying impulsive interference.
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