PFI-TT: Artificial Intelligence-enabled Real-time System for Early Epileptic Seizure Detection and Prediction

这一创新-技术转化伙伴关系(PFI-TT)项目的更广泛影响/商业潜力将是对耐药癫痫患者的癫痫发作预测领域。开发的这项技术可以对超过340万患有癫痫的美国人产生影响，其中包括100万患有抗药性癫痫的人。目前可供癫痫患者使用的基础设施不足，而且大多是被动的，即在癫痫发作后提供支持。身体伤害、社会排斥(情感伤害)或机会有限(经济伤害)是由于预测规模的能力不足造成的。拟议的基于人工智能(AI)的模型将被整合到可穿戴传感器中，以检测大脑电活动的异常。这项技术将被整合到智能手机等设备中，并且将是非侵入性的，而且成本很低。Siezure预测可以使及时的人类缓解措施成为可能，从而通过降低急诊室成本、提高生活质量并允许护理人员提供预防措施(如抗癫痫药物)来提供价值。随着癫痫抢救疗法的最新进展，拟议的早期预测技术可以帮助患者更好地决定何时用药防止癫痫发作。拟议的项目将设计和开发先进的机器学习算法，以识别可用于使用可穿戴式脑电(EEG)数据预测癫痫发作的神经标记物。该项目的目标是提供能够以高灵敏度和低假阳性率预测癫痫发作的计算基础设施，并能够提供实时连续监测，使其对患者和护理人员产生高度影响。这些解决方案将通过制定深度学习模型来开发，该模型将结合残留和长期-短期记忆(LSTM)层进行特征提取，以提高对类别失衡的敏感度和特异度。在这一发展之后，将使用完全连接层进行预测。为了确保通用性，这些模型将使用来自不同脑电数据采集点和技术的数据进行训练和测试。EDGE/联邦计算基础设施将被制定为提醒患者和护理人员对即将到来的癫痫采取预防措施，从而为患者带来更好的结果。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project will be in the area of seizure prediction for patients suffering from drug-resistant epilepsy. The technology developed can have impact on more than 3.4 million Americans that suffer from epilepsy – including 1 million who suffer from drug-resistant epilepsy. Current infrastructure available to the epileptic population is inadequate and is mostly reactive i.e., support is provided after a seizure attack. Physical injury, social ostracization (emotional injury), or limited opportunities (economic injury) result from the inadequate ability to predict siezures. The proposed Artificial Intelligence (AI)-based models will be incorporated into wearable sensors that detect abnormalities in brain electrical activity. The technology will be incorporated into devices like smart phones, and will be non-invasive, and low-cost. Siezure prediction can enable timely human mitigation measures thus providing value by reducing emergency room costs, improving quality of life, and allowing caregivers to provide precautionary measures such as anti-seizure medications. With recent advances in seizure rescue therapeutics, the proposed early prediction technology can help patients make better decisions on when to medicate to prevent a seizure. The proposed project will design and develop advanced machine learning algorithms to identify neuromarkers that can be used for the prediction of epileptic seizures using data from wearable electroencephalography (EEG). The goal of this project is to provide computational infrastructure that can predict seizures with high sensitivity and low false positive rates, and can provide real-time continuous monitoring making it highly impactful for patients and caregivers. These solutions will be developed by formulating deep-learning models that will combine residual and long-short term memory(LSTM) layers for feature extraction for improved sensitivity and specificity for class imbalances. This development will be followed by prediction using fully connected layers. To ensure generalizability, the models will be trained and tested using data from various EEG data acquisition sites and techniques. The edge/federated computing infrastructure will be formulated to alert patients and caregivers to take preventative measures about an impending seizure resulting in better outcomes for the patients.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.