RELIEVE: First Closed-loop non-Invasive Seizure Prevention System

RELIEVE：首个闭环非侵入性癫痫预防系统

• 批准号: 10070072
• 金额: $ 55.11万
• 依托单位: THE UNIVERSITY OF MANCHESTER
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10070072
• 关键词: RELIEVE; First; Closed; loop; non

The goal of Project RELIEVE is to build the very first non-invasive effective closed-loop monitoring and intervention system for brain related disorders. The outcomes can be used to treat or manage various psychiatric and neurological disorders. We do this by pushing the technological boundaries in two separate domains. (1) AI domain: We develop a class of mathematical and algorithmic tools for brain data based on the recently developed mathematical theories that are potentially useful for the real-time monitoring of the brain data. Such advancements have shown promise in robotics and self-driving cars and have high potential to work efficiently in highly dynamic environments where personalization and low computation power is a must. This makes such algorithms runnable using brain data such as EEG (electric activity on the head surface) on ordinary wearable devices. (2) Neurostimulation domain: We combine two characteristics of the ultrasound waves in stimulation and imaging of the nervous system to build the first smart-navigated wearable ultrasound patch. We also choose the vagus nerve as the target for neurostimulation as one of the most promising sites to interact with the nervous system with proven implications for a large spectrum of neurological and psychiatric disorders (e.g. dementia, depression, epilepsy, etc.). We call this unit 'WU-VNS' standing for non-invasive wearable ultrasound vagus nerve stimulation. In the next three years, we will use epilepsy as the first use case of the developed technologies to train and test a closed-loop system. For this, the AI monitors the brain through a patch that records EEG in addition to other physiological measures such as heart rate and motion. Upon prediction of a forthcoming anomaly (seizure in this case) by the AI unit, the neurostimulation module activates and stimulates the vagus nerve non-invasively. During this process also a so-called 'active learning' happens in which the AI learns from the reactions of the nervous system to the stimulation protocol and can fine-tune the protocol for future interventions. To achieve this, we have designed a complex phase-based development and testing plan: The first two generations (Gen. 1 and 2) are the intermediate versions of the full system and act in open loops validating each of the AI and neurostimulation subsystems. These two generations already have high potentials to independently turn into medical device products with large market needs. Ultimately, in Gen. 3 we close the loop by integrating the AI and the WU VNS and consequently validate the efficacy and the usability of the system.

RELIEVE项目的目标是建立第一个针对大脑相关疾病的无创有效闭环监测和干预系统。结果可用于治疗或控制各种精神和神经疾病。我们通过突破两个不同领域的技术界限来做到这一点。 （1）人工智能领域：我们基于最近发展的数学理论开发了一类针对大脑数据的数学和算法工具，这些工具可能对大脑数据的实时监测有用。这些进步在机器人和自动驾驶汽车领域显示出了前景，并且在必须个性化和低计算能力的高度动态环境中具有高效工作的巨大潜力。这使得此类算法可以在普通可穿戴设备上使用脑电图（EEG）（头部表面的电活动）等大脑数据来运行。 （2）神经刺激领域：我们结合了超声波对神经系统的刺激和成像的两个特性，构建了第一个智能导航可穿戴超声贴片。我们还选择迷走神经作为神经刺激的目标，它是与神经系统相互作用的最有希望的部位之一，已被证明对广泛的神经和精神疾病（例如痴呆、抑郁、癫痫等）具有影响。我们将该装置称为“WU-VNS”，代表非侵入性可穿戴超声迷走神经刺激。在接下来的三年中，我们将使用癫痫作为所开发技术的第一个用例来训练和测试闭环系统。为此，人工智能通过一个记录脑电图以及心率和运动等其他生理指标的贴片来监控大脑。当人工智能单元预测即将出现的异常情况（在本例中为癫痫发作）时，神经刺激模块会以非侵入方式激活并刺激迷走神经。在此过程中，还会发生所谓的“主动学习”，其中人工智能从神经系统对刺激方案的反应中学习，并可以微调方案以进行未来的干预。为了实现这一目标，我们设计了一个复杂的分阶段开发和测试计划：前两代（第一代和第二代）是整个系统的中间版本，并以开环方式运行，验证每个人工智能和神经刺激子系统。这两代产品已经具备独立转化为市场需求量大的医疗器械产品的巨大潜力。最终，在第 3 代中，我们通过集成 AI 和 WU VNS 来闭合循环，从而验证系统的功效和可用性。