Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdimensional (HD) computing has evolved in recent years as a new promising machine learning approach, especially when talking about wearable applications. But in the case of epilepsy detection, standard HD computing is not performing at the level of other state-of-the-art algorithms. This could be due to the inherent complexity of the seizures and their signatures in different biosignals, such as the electroencephalogram (EEG), the highly personalized nature, and the disbalance of seizure and non-seizure instances. In the literature, different strategies for improved learning of HD computing have been proposed, such as iterative (multi-pass) learning, multi-centroid learning and learning with sample weight ("OnlineHD"). Yet, most of them have not been tested on the challenging task of epileptic seizure detection, and it stays unclear whether they can increase the HD computing performance to the level of the current state-of-the-art algorithms, such as random forests. Thus, in this paper, we implement different learning strategies and assess their performance on an individual basis, or in combination, regarding detection performance and memory and computational requirements. Results show that the best-performing algorithm, which is a combination of multi-centroid and multi-pass, can indeed reach the performance of the random forest model on a highly unbalanced dataset imitating a real-life epileptic seizure detection application.
翻译:由于实时检测和可磨损设备设计的挑战,超维(HD)计算近年来演变为新的有希望的机器学习方法,特别是在谈论可磨损应用程序时。但是,在癫痫检测方面,标准的HD计算并不是在其他最先进的算法(“在线HD”)的水平上进行。这可能是由于缉获及其在不同生物信号(例如电脑图(EEEEG)、高度个性化性质以及缉获和非震荡实例的不平衡)中的签名具有内在复杂性,但仍是一个未实现的目标。在文献中,提出了改进HD计算学习的不同战略,例如反复(多路)学习、多中心学习和用样本重量(“在线HD”)学习。然而,大多数标准HD计算没有在具有挑战性的癫痫检测任务中进行测试,而且它仍然不清楚它们是否能提高HD的多面性能,以及缉获和非震荡的不均匀现象。在文献中,对森林进行最佳的模拟和混合计算性能进行评估,从而显示我们当前水平的测算结果。