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

PFI-TT：用于早期癫痫发作检测和预测的人工智能实时系统

• 批准号: 2213951
• 负责人: Fahad Saeed
• 金额: $ 25万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213951&HistoricalAwards=false
• 关键词: PFI; TT; Artificial; Intelligence; enabled

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.

该创新-技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力将在耐药性癫痫患者的癫痫发作预测领域。 这项技术可以对340多万患有癫痫的美国人产生影响，其中包括100万患有耐药性癫痫的人。癫痫人群现有的基础设施不足，而且大多是被动的，即，在癫痫发作后提供支助。 身体伤害，社会排斥（情感伤害），或有限的机会（经济伤害）的结果，从能力不足，以预测siezures。拟议中的基于人工智能（AI）的模型将被纳入可穿戴传感器中，以检测脑电活动的异常。该技术将被整合到智能手机等设备中，并且将是非侵入性的，低成本的。Siezure预测可以及时采取人类缓解措施，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，提出的早期预测技术可以帮助患者更好地决定何时进行预防癫痫发作。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脑电图（EEG）数据预测癫痫发作的神经标志物。该项目的目标是提供计算基础设施，可以高灵敏度和低假阳性率预测癫痫发作，并可以提供实时连续监测，使其对患者和护理人员具有高度影响力。这些解决方案将通过制定深度学习模型来开发，该模型将结合联合收割机残差和长短期记忆（LSTM）层进行特征提取，以提高类别不平衡的灵敏度和特异性。这一发展之后将使用全连接层进行预测。为了确保通用性，将使用来自各种EEG数据采集站点和技术的数据对模型进行训练和测试。边缘/联合计算基础设施将被制定为提醒患者和护理人员采取预防措施，以预防即将发生的癫痫发作，从而为患者带来更好的结果。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SPERTL: Epileptic Seizure Prediction using EEG with ResNets and Transfer Learning

• DOI: 10.1109/bhi56158.2022.9926767
• 发表时间: 2022-09
• 期刊: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
• 作者: Umair Mohammad;Fahad Saeed

Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks

• DOI: 10.1109/bigdata55660.2022.10021070
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Tianren Yang;Mai A. Al-Duailij;S. Bozdag;Fahad Saeed