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脑相关疾病的非侵入性有效闭环监测和干预系统。其结果可用于治疗或管理各种精神和神经系统疾病。我们通过在两个不同的领域推动技术边界来做到这一点。(1)AI领域：我们开发了一类数学和算法工具的大脑数据的基础上，最近开发的数学理论，是潜在的有用的实时监测的大脑数据。这些进步已经在机器人和自动驾驶汽车领域显示出了希望，并且具有在高度动态环境中高效工作的巨大潜力，在这种环境中，个性化和低计算能力是必须的。这使得这样的算法可以在普通的可穿戴设备上使用大脑数据，如EEG（头部表面的电活动）。(2)神经刺激域：我们结合联合收割机超声波对神经系统的刺激和成像两个特点，构建了第一个智能导航的可穿戴超声贴片。我们还选择迷走神经作为神经刺激的靶点，因为迷走神经是与神经系统相互作用的最有希望的部位之一，已证明对多种神经和精神疾病（例如痴呆、抑郁症、癫痫等）有影响。我们称这个单元为“WU-VNS”，代表非侵入性可穿戴超声迷走神经刺激。在接下来的三年里，我们将使用癫痫作为开发技术的第一个用例，以训练和测试闭环系统。为此，人工智能通过一个贴片来监测大脑，除了记录心率和运动等其他生理指标外，还记录EEG。在AI单元预测即将发生的异常（在这种情况下为癫痫发作）时，神经刺激模块非侵入性地激活并刺激迷走神经。在这个过程中，也会发生所谓的“主动学习”，其中AI从神经系统对刺激协议的反应中学习，并可以微调协议以进行未来的干预。为了实现这一目标，我们设计了一个复杂的基于阶段的开发和测试计划：前两代（Gen 1和Gen 2）是整个系统的中间版本，并在开环中验证每个AI和神经刺激子系统。这两代产品已经具备了很高的潜力，可以独立转化为具有巨大市场需求的医疗器械产品。最终，在Gen.3中，我们通过集成AI和WU VNS来闭合循环，从而验证系统的有效性和可用性。