结合通用模型与个性化模型基于超维计算的癫痫检测
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing
AI总结：
本研究利用超维计算构建和存储模型的特性，提出结合个性化与通用模型创建混合模型的方法，提升了可穿戴设备癫痫检测性能，并从工程与神经学双重视角提供了新见解。
摘要：
癫痫是一种患病率很高的慢性神经系统疾病。然而，目前仍缺乏足够的技术支持来实现日常生活中的癫痫检测和门诊连续监测。超维（HD）计算是可穿戴设备的一种极具前景的替代方案，其特点是学习过程更简单，内存需求更低。在本研究中，我们展示了HD计算及其模型构建和存储方式的一些额外特性，可用于进一步理解、比较和创建更先进的癫痫检测机器学习模型。这些可能性在其他最先进的模型（如随机森林或神经网络）中是无法实现的。我们比较了不同类别（发作和非发作）下模型的跨受试者相似性，然后研究了从个性化模型创建通用模型的过程，最后研究了如何结合个性化模型和通用模型来创建混合模型。这提高了癫痫检测的性能。我们还在两个不同数据集上测试了模型间的知识转移。最后，所有这些示例不仅从工程角度（为可穿戴设备创建更好的模型）非常有趣，而且从神经学角度（更好地理解个体癫痫模式）也极具吸引力。
英文摘要：
Epilepsy is a chronic neurological disorder with a significant prevalence. However, there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing is an interesting alternative for wearable devices, characterized by a much simpler learning process and also lower memory requirements. In this work, we demonstrate a few additional aspects in which HD computing, and the way its models are built and stored, can be used for further understanding, comparing, and creating more advanced machine learning models for epilepsy detection. These possibilities are not feasible with other state-of-the-art models, such as random forests or neural networks. We compare inter-subject similarity of models per different classes (seizure and non-seizure), then study the process of creation of generalized models from personalized ones, and in the end, how to combine personalized and generalized models to create hybrid models. This results in improved epilepsy detection performance. We also tested knowledge transfer between models created on two different datasets. Finally, all those examples could be highly interesting not only from an engineering perspective to create better models for wearables, but also from a neurological perspective to better understand individual epilepsy patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
