Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Machine Learning Approach to Automatic Classification of Eight Sleep
  Disorders   In this research, we attempt to answer the following basic research
questions: Is a machine learning model able to classify all types of sleep
disorders with high accuracy? Among the different modalities of sleep disorder
signals, are some more important than others? Do raw signals improve the
performance of a deep learning model when they are used as inputs? Prior
research showed that most sleep disorders belong to eight categories. To study
the performance of machine learning models in classifying polysomnography
recordings into the eight categories of sleep pathologies, we selected the
Cyclic Alternating Pattern Sleep Database. We developed a multi-channel Deep
Learning model where a set of Convolutional Neural Networks were applied to six
channels of raw signals of different modalities, including three channels of
EEG signals and one channel each of EMG, ECG , and EOG signals. To compare the
performance of the DL model with other models, we designed a model that took
spectral features, instead of raw signals, as its inputs. We first studied the
"importance" issue of signal modalities using the RF algorithm. We found that
ECG contributed most to the important features and EMG second, among the four
signal modalities. We then studied the accuracy performance of the proposed
machine learning models. We verified that the multi-channel DL-R model, which
took raw signals as its inputs, outperformed all other models, with its
sensitivity and specificity scores both being above 95 %. This accuracy
performance is on a par with those published results which dealt with fewer
types of sleep disorders. We adopted two popular heatmap-generating techniques,
with which we confirmed that the DL model's superior performance was owing to
the CNN network's ability to extract potent features from raw signals.

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The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci