Ubiquitous bio-sensing for personalized health monitoring is slowly becoming
a reality with the increasing availability of small, diverse, robust, high
fidelity sensors. This oncoming flood of data begs the question of how we will
extract useful information from it. In this paper we explore the use of a
variety of representations and machine learning algorithms applied to the task
of seizure detection in high resolution, multichannel EEG data. We explore
classification accuracy, computational complexity and memory requirements with
a view toward understanding which approaches are most suitable for such tasks
as the number of people involved and the amount of data they produce grows to
be quite large. In particular, we show that layered learning approaches such as
Deep Belief Networks excel along these dimensions.

本文探讨了在高分辨率，多通道 EEG 数据中，采用各种表示方法和机器学习算法进行癫痫检测的可行性，通过研究分类精度、计算复杂度和内存需求了解哪些方法最适合处理大规模数据。特别是指出，堆叠学习方法，如深度置信网络，在这些方面表现出色。