software . 2018

Deep Convolutional Neural Networks For Interpretable Analysis Of Eeg Sleep Stage Scoring

Vilamala, Albert; Madsen, Kristoffer H.; Hansen, Lars K.;
Open Source
  • Published: 07 Feb 2018
  • Publisher: Zenodo
Abstract
Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of highly trained professionals. Consequently, research efforts to purse for an automatic stage scoring based on machine learning techniques have been carried out over the last years. In this work, we resort to multitaper spectral analysis to create visually interpretable images of sleep patterns from EEG signals as inputs to a deep convolutional network trained to solve visual recognition tasks. As a working example of transfer l...
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Funded by
EC| MINDS
Project
MINDS
Multivariate analysis for the Imaging of Neuronal activity using Deep architectureS
  • Funder: European Commission (EC)
  • Project Code: 659860
  • Funding stream: H2020 | MSCA-IF-EF-ST
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Software . 2018
Provider: Datacite
Zenodo
Software . 2018
Provider: Datacite
Zenodo
Software . 2018
Provider: Datacite
Zenodo
Software . 2018
Provider: Datacite
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