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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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Abstract

Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimisation steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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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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