publication . Other literature type . Conference object . 2020

Noise-Resilient and Interpretable Epileptic Seizure Detection

Hitchcock Thomas, Anthony; Aminifar, Amir; Atienza, David;
Open Access
  • Published: 17 May 2020
  • Publisher: Zenodo
  • Country: Switzerland
Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over methods relying on hand-crafted features. However, neural networks are susceptible to confounding artifacts commonly present in EEG signals and are notoriously difficult to interpret. In this work, we present a neural-network based algorithm for seizure detection which leverages recent advances in information theory to construct a signal representation containing the minimal amount of information necessary to ...
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
Related Organizations
Funded by
EC| DeepHealth
Deep-Learning and HPC to Boost Biomedical Applications for Health
  • Funder: European Commission (EC)
  • Project Code: 825111
  • Funding stream: H2020 | IA
Download fromView all 5 versions
Other literature type . 2020
Provider: Datacite
Conference object . 2020
Provider: ZENODO
Other literature type . 2020
Provider: Datacite
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue