English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Ubiquitous bio-sensing for personalized health monitoring is slowly becominga reality with the increasing availability of small, diverse, robust, highfidelity sensors. This oncoming flood of data begs the question of how we willextract useful information from it. In this paper we explore the use of avariety of representations and machine learning algorithms applied to the taskof seizure detection in high resolution, multichannel EEG data. We exploreclassification accuracy, computational complexity and memory requirements witha view toward understanding which approaches are most suitable for such tasksas the number of people involved and the amount of data they produce grows tobe quite large. In particular, we show that layered learning approaches such asDeep Belief Networks excel along these dimensions.

Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection