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Energy efficient convolutional neural networks for arrhythmia detection

View ORCID ProfileNikoletta Katsaouni, Florian Aul, Lukas Krischker, Sascha Schmalhofer, Lars Hedrich, View ORCID ProfileMarcel H. Schulz
doi: https://doi.org/10.1101/2021.09.23.461522
Nikoletta Katsaouni
aInstitute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
cGerman Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590, Frankfurt am Main, Germany
dCardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
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  • ORCID record for Nikoletta Katsaouni
  • For correspondence: katsaouni@em.uni-frankfurt.de
Florian Aul
bInstitute for Computer Science, Goethe University Frankfurt, Germany
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Lukas Krischker
bInstitute for Computer Science, Goethe University Frankfurt, Germany
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Sascha Schmalhofer
bInstitute for Computer Science, Goethe University Frankfurt, Germany
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Lars Hedrich
bInstitute for Computer Science, Goethe University Frankfurt, Germany
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Marcel H. Schulz
aInstitute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
cGerman Center for Cardiovascular Research (DZHK), Partner site RheinMain, 60590, Frankfurt am Main, Germany
dCardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
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  • ORCID record for Marcel H. Schulz
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Posted September 24, 2021.
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Energy efficient convolutional neural networks for arrhythmia detection
Nikoletta Katsaouni, Florian Aul, Lukas Krischker, Sascha Schmalhofer, Lars Hedrich, Marcel H. Schulz
bioRxiv 2021.09.23.461522; doi: https://doi.org/10.1101/2021.09.23.461522
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Energy efficient convolutional neural networks for arrhythmia detection
Nikoletta Katsaouni, Florian Aul, Lukas Krischker, Sascha Schmalhofer, Lars Hedrich, Marcel H. Schulz
bioRxiv 2021.09.23.461522; doi: https://doi.org/10.1101/2021.09.23.461522

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