SCH: INT: Collaborative Research: Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy

本课题的研究目标是：创伤后多模态信号分析与数据融合 癫痫预测，与南加州大学的Pl Dominique邓肯合作，是为了预测 创伤性脑损伤（TBI）后癫痫发作，使用机器的创新分析工具 学习和应用数学从多模态数据集中识别癫痫样活动的特征 从动物模型和人类患者中收集。这项研究将加速 从一个丰富的数据集中发现了TBI后癫痫发生的显著和强大的特征，这些数据集收集自 抗癫痫治疗的癫痫生物信息学研究（EpiBioS4Rx），因为它正在被 研究当代机器学习理论中最先进的模型、方法和算法。 这种对数据的二次使用支持从聚合记录中自动发现可靠的知识 动物模型和人类患者的数据将导致创新的模型来预测创伤后癫痫 （PTE）。这种基于机器学习的丰富数据集调查补充了正在进行的数据采集 和经典的基于生物药理学的分析正在进行中的研究，并可能导致严格的结果， 抗癫痫治疗的发展，可以预防这种疾病。确定以下方面的突出特点： 时间序列和图像，以帮助设计一个预测PTE使用的数据，从两个物种和多个人 在异质TBI条件下，提出了需要解决的重大理论挑战。在这 项目，建议采用迁移学习和领域适应的观点来实现这些目标 在两个人群的多模态生物医学数据集的目标。具体地说， 将利用学习文献来增加数据，在模型组件之间共享参数，以减少 需要优化的参数数量，并使用最先进的架构来开发模型 用于特征提取。这些将与手工特征提取的既定管道进行比较 严格的交叉验证分析。开发的迁移学习技术将能够提取 在动物和人类数据中推广的功能。此外，这些理论技术与 相关的模型和优化方法将适用于其他多物种迁移学习 在卫生和医疗方面可能出现的挑战。多模态特征提取和 使用新型分类器进行疾病发作预测的判别模型学习还提供了以下见解 通过联合多模态数据分析使用先进的机器学习技术发现生物标志物。

The research objective of this proposal, Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy Prediction, with Pl Dominique Duncan from the University of Southern California, is to predict the onset of epileptic seizures following traumatic brain injury (TBI), using innovative analytic tools from machine learning and applied mathematics to identify features of epileptiform activity, from a multimodal dataset collected from both an animal model and human patients. The proposed research will accelerate the discovery of salient and robust features of epileptogenesis following TBI from a rich dataset, collected from the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), as it is being acquired by investigating state-of-the-art models, methods, and algorithms from contemporary machine learning theory. This secondary use of data to support automated discovery of reliable knowledge from aggregated records of animal model and human patient data will lead to innovative models to predict post-traumatic epilepsy (PTE). This machine learning based investigation of a rich dataset complements ongoing data acquisition and classical biostatistics-based analyses ongoing in the study and can lead to rigorous outcomes for the development of antiepileptogenic therapies, which can prevent this disease. Identifying salient features in time series and images to help design a predictor of PTE using data from two species and multiple individuals with heterogeneous TBI conditions presents significant theoretical challenges that need to be tackled. In this project, it is proposed to adopt transfer learning and domain adaptation perspectives to accomplish these goals in multimodal biomedical datasets across two populations. Specifically, techniques emerging from d,eep learning literature will be exploited to augment data, share parameters across model components to reduce the number of parameters that need to be optimized, and use state-of-the-art architectures to develop models for feature extraction. These will be compared against established pipelines of hand-crafted feature extraction in rigorous cross-validation analyses. Developed techniques for transfer learning will be able to extract features that generalize across animal and human data. Moreover, these theoretical techniques with associated models and optimization methods will be applicable to other multi-species transfer learning challenges that may arise in the context of health and medicine. Multimodal feature extraction and discriminative model learning for disease onset prediction using novel classifiers also offer insights into biomarker discovery using advanced machine learning techniques through joint multimodal data analysis.