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

项目的目标是建立首个无创有效的脑相关疾病闭环监测和干预系统。结果可用于治疗或管理各种精神和神经疾病。我们通过在两个不同的领域推动技术边界来实现这一点。(1)人工智能领域：基于最近发展的数学理论，我们开发了一类用于大脑数据的数学和算法工具，这些数学和算法工具可能对大脑数据的实时监测有用。这些进步已经在机器人和自动驾驶汽车中显示出前景，并且在高度动态的环境中具有很大的潜力，在这些环境中，个性化和低计算能力是必须的。这使得这种算法可以使用普通可穿戴设备上的脑电图（EEG）等大脑数据来运行。(2)神经刺激领域：结合超声波在神经系统的刺激和成像两方面的特点，构建首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，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，已被证明对许多神经和精神疾病（如痴呆，抑郁症，癫痫等）有影响。我们称这种装置为“WU-VNS”，即无创可穿戴超声迷走神经刺激装置。在未来三年内，我们将把癫痫作为开发的技术的第一个用例，以培训和测试一个闭环系统。为此，人工智能通过一个贴片来监测大脑，该贴片除了记录心率和运动等其他生理指标外，还记录脑电图。当人工智能单元预测到即将发生的异常（在这种情况下是癫痫发作）时，神经刺激模块会激活并非侵入性地刺激迷走神经。在这个过程中，也会发生所谓的“主动学习”，人工智能从神经系统对刺激方案的反应中学习，并可以为未来的干预调整方案。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2）是整个系统的中间版本，并在开环中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具有较大市场需求的医疗器械产品的巨大潜力。最终，在Gen. 3中，我们通过集成AI和WU VNS完成了闭环，从而验证了系统的有效性和可用性。

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.