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A new deep brain stimulation waveform based on PWM

机译:基于PWM的新型深部脑刺激波形

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This paper proposes a non-periodic shape optimization based on PWM principle to identify the optimal parameters of deep brain stimulation (DBS) waveforms. A computational model characterizing Parkinson's disease (PD) is introduced. When PWM DBS is injected into the networks, the tonic rhythm output of GPi neurons could restore the thalamic relay properties. By comparing with the periodic rectangular waveforms, we demonstrate the view that the PWM waveforms can not only guarantee the reliability, but also can decrease energy requirements of stimulation in vivo, that means, it can increase the lifetime of the DBS electrodes, decrease the stimulation-induced tissue damage, then improve the quality of life for PD patient.
机译:本文提出了一种基于PWM原理的非周期性形状优化算法,以识别深部脑刺激(DBS)波形的最佳参数。介绍了表征帕金森氏病(PD)的计算模型。当将PWM DBS注入网络时,GPi神经元的强音节律输出可以恢复丘脑中继特性。通过与周期性矩形波形进行比较,我们证明了PWM波形不仅可以保证可靠性,而且可以降低体内刺激的能量需求,这可以增加DBS电极的寿命,减少刺激引起的组织损伤,从而改善PD患者的生活质量。

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