Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks with Weighted Averaged Overnight Airflow Features
  for Sleep Apnea-Hypopnea Severity Classification   Dramatic raising of Deep Learning (DL) approach and its capability in
biomedical applications lead us to explore the advantages of using DL for sleep
Apnea-Hypopnea severity classification. To reduce the complexity of clinical
diagnosis using Polysomnography (PSG), which is multiple sensing platform, we
incorporates our proposed DL scheme into one single Airflow (AF) sensing signal
(subset of PSG). Seventeen features have been extracted from AF and then fed
into Deep Neural Networks to classify in two studies. First, we proposed a
binary classifications which use the cutoff indices at AHI = 5, 15 and 30
events/hour. Second, the multiple Sleep Apnea-Hypopnea Syndrome (SAHS) severity
classification was proposed to classify patients into 4 groups including no
SAHS, mild SAHS, moderate SAHS, and severe SAHS. For methods evaluation, we
used a higher number of patients than related works to accommodate more
diversity which includes 520 AF records obtained from the MrOS sleep study
(Visit 2) database. We then applied the 10-fold cross-validation technique to
get the accuracy, sensitivity and specificity. Moreover, we compared the
results from our main classifier with other two approaches which were used in
previous researches including the Support Vector Machine (SVM) and the
Adaboost-Classification and Regression Trees (AB-CART). From the binary
classification, our proposed method provides significantly higher performance
than other two approaches with the accuracy of 83.46 %, 85.39 % and 92.69 % in
each cutoff, respectively. For the multiclass classification, it also returns a
highest accuracy of all approaches with 63.70 %.

23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
10
The categories of this paper are: q-bio