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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
Florian Aul
bInstitute for Computer Science, Goethe University Frankfurt, Germany
Lukas Krischker
bInstitute for Computer Science, Goethe University Frankfurt, Germany
Sascha Schmalhofer
bInstitute for Computer Science, Goethe University Frankfurt, Germany
Lars Hedrich
bInstitute for Computer Science, Goethe University Frankfurt, Germany
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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Posted September 24, 2021.
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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