Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Sequence Learning for Accurate Gestational Age Estimation from a
  $\$$25 Doppler Device   Assessing fetal development is usually carried out by techniques such as
ultrasound imaging, which is generally unavailable in rural areas due to the
high cost, maintenance, skills and training needed to operate the devices
effectively. In this work, we propose a low-cost one-dimensional Doppler-based
method for estimating gestational age (GA). Doppler time series were collected
from 401 pregnancies between 5 and 9 months GA using a smartphone. The proposed
model for GA estimation is based on sequence learning by forming a temporally
dependent model using a convolutional long-short-term memory network.
Time-frequency features are extracted from Doppler signals and regularized
before feeding to the network. The overall mean absolute GA error with respect
to the last menstrual period was found to be 0.71 month, which outperforms all
previous works.

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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph