基于相位振幅耦合的嵌入式帕金森病闭环平台

Molly Alexandre, Song Luan, Z. Mari, W. Anderson, Y. Salimpour, T. Constandinou, L. Grand
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引用次数: 1

摘要

脑深部电刺激(DBS)是临床上广泛应用的治疗帕金森病左旋多巴治疗的顽固性症状和并发症的治疗方式。目前可用的dbs系统采用连续开环增产策略。它可能是多余的,否则我们可以延长电池寿命。最近,在接受DBS手术的患者的运动皮层中发现了帕金森病的强大电生理特征。β - γ相幅耦合(PAC)的减少与症状改善和DBS本身的治疗效果相关。我们的目标是开发一种微型的、可植入的、自适应的系统,当适当的PAC算法输出触发时,它只刺激神经目标。作为第一步,在本文中,我们通过使用帕金森病患者术中记录的人类数据来比较已发表的PAC算法。然后,我们引入IIR掩蔽,以便稍后实现快速和低功耗的FPGA实现PAC映射,用于术中研究。我们的闭环应用预计比目前的DBS系统消耗更少的功率,因此我们可以在不影响临床效益的情况下延长电池寿命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Embedded Phase-Amplitude Coupling Based Closed-loop Platform for Parkinson's Disease
Deep Brain Stimulation (DBS) is a widely used clinical therapeutic modality to treat Parkinsons disease refractory symptoms and complications of levodopa therapy. Currently available DBSsystems use continuous, open-loop stimulation strategies. It might be redundant and we could extend the battery life otherwise. Recently, robust electrophysiological signatures of Parkinsons disease have been characterized in motor cortex of patients undergoing DBS surgery. Reductions in the beta-gamma Phase-Amplitude coupling (PAC) correlated with symptom improvement, and the therapeutic effects of DBS itself. We aim to develop a miniature, implantable and adaptive system, which only stimulates the neural target, when triggered by the output of the appropriate PAC algorithm. As a first step, in this paper we compare published PAC algorithms by using human data intra-operatively recorded from Parkinsonian patients. We then introduce IIR masking for later achieving fast and low-power FPGA implementation of PAC mapping for intra-operative studies. Our closed-loop application is expected to consume significantly less power than current DBS systems, therefore we can increase the battery life, without compromising clinical benefits.
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