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基于 EEG 的闭环可穿戴式超声深脑刺激系统在小鼠中的应用。

Closed-loop wearable ultrasound deep brain stimulation system based on EEG in mice.

机构信息

Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, People's Republic of China.

Institute of Biomedical and Health engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, People's Republic of China.

出版信息

J Neural Eng. 2021 Aug 31;18(4). doi: 10.1088/1741-2552/ac1d5c.

Abstract

. Epilepsy is one of the most common severe brain disorders. Ultrasound deep brain stimulation (UDBS) has shown a potential capability to suppress seizures. However, because seizures occur sporadically, it is necessary to develop a closed-loop system to suppress them. Therefore, we developed a closed-loop wearable UDBS system that delivers ultrasound to the hippocampus to suppress epileptic seizures.Mice were intraperitoneally injected with 10 mg kgkainic acid and divided into sham and UDBS groups. Epileptic seizures were detected by applying both long short-term memory (LSTM) and bidirectional LSTM (BILSTM) networks according to EEG signal characteristics. When epileptic seizures were detected, the closed-loop UDBS system automatically activated a trigger switch to stimulate the hippocampus for 10 min and continuously record EEG signals until 20 min after ultrasonic stimulation. EEG signals were analyzed using the MATLAB software. After EEG recording, we observed the survival rate of the experimental mice for 72 h.The BiLSTM network was found to have preferable classification performance over the LSTM network. The closed-loop UDBS system with BiLSTM could automatically detect epileptic seizures using EEG signals and effectively reduce epileptic EEG power spectral density and seizure duration by 10.73%, eventually improving the survival rate of early epileptic mice from 67.57% in the sham group to 88.89% in the UDBS group.The closed-loop UDBS system developed in this study could be an effective clinical tool for the control of epilepsy.

摘要

癫痫是最常见的严重脑部疾病之一。超声深部脑刺激 (UDBS) 已显示出抑制癫痫发作的潜力。然而,由于癫痫发作是偶发性的,因此需要开发一个闭环系统来抑制它们。因此,我们开发了一种闭环可穿戴式 UDBS 系统,该系统将超声传递到海马体以抑制癫痫发作。

将 10mgkg 海人酸腹腔注射到小鼠中,并将其分为假手术和 UDBS 组。根据 EEG 信号特征,应用长短期记忆(LSTM)和双向 LSTM(BILSTM)网络来检测癫痫发作。当检测到癫痫发作时,闭环 UDBS 系统会自动激活触发开关,以 10 分钟的时间刺激海马体,并持续记录 EEG 信号,直到超声刺激后 20 分钟。使用 MATLAB 软件分析 EEG 信号。

在 EEG 记录后,我们观察了实验小鼠 72 小时的存活率。发现 BILSTM 网络比 LSTM 网络具有更好的分类性能。具有 BILSTM 的闭环 UDBS 系统可以使用 EEG 信号自动检测癫痫发作,并有效降低癫痫 EEG 功率谱密度和发作持续时间 10.73%,最终将早期癫痫小鼠的存活率从假手术组的 67.57%提高到 UDBS 组的 88.89%。

本研究开发的闭环 UDBS 系统可能成为癫痫控制的有效临床工具。

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