Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating an Explainable ECG Beat Space With Variational Auto-Encoders   Electrocardiogram signals are omnipresent in medicine. A vital aspect in the
analysis of this data is the identification and classification of heart beat
types which is often done through automated algorithms. Advancements in neural
networks and deep learning have led to a high classification accuracy. However,
the final adoption of these models into clinical practice is limited due to the
black-box nature of the methods. In this work, we explore the use of
variational auto-encoders based on linear dense networks to learn human
interpretable beat embeddings in time-series data. We demonstrate that using
this method, an interpretable and explainable ECG beat space can be generated,
set up by characteristic base beats.

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The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg