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<title>A Bayesian neural network approach to compare the spectral information from nasal pressure and thermistor airflow in the automatic sleep apnea severity estimation</title>
<creator>Gutierrez Tobal, Gonzalo César</creator>
<creator>Frutos Arribas, Julio Fernando de</creator>
<creator>Álvarez González, Daniel</creator>
<creator>Vaquerizo Villar, Fernando</creator>
<creator>Barroso García, Verónica</creator>
<creator>Crespo Senado, Andrea</creator>
<creator>Campo Matias, Félix del</creator>
<creator>Hornero Sánchez, Roberto</creator>
<description>Producción Científica</description>
<description>In the sleep apnea-hypopnea syndrome (SAHS)&#xd;
context, airflow signal plays a key role for the simplification of&#xd;
the diagnostic process. It is measured during the standard&#xd;
diagnostic test by the acquisition of two simultaneous sensors: a&#xd;
nasal prong pressure (NPP) and a thermistor (TH). The&#xd;
current study focuses on the comparison of their spectral&#xd;
content to help in the automatic SAHS-severity estimation. The&#xd;
spectral analysis of 315 NPP and corresponding TH recordings&#xd;
is firstly proposed to characterize the conventional band of&#xd;
interest for SAHS (0.025-0.050 Hz.). A magnitude squared&#xd;
coherence analysis is also conducted to quantify possible&#xd;
differences in the frequency components of airflow from both&#xd;
sensors. Then, a feature selection stage is implemented to assess&#xd;
the relevance and redundancy of the information extracted&#xd;
from the spectrum of NPP and TH airflow. Finally, a multiclass&#xd;
Bayesian multi-layer perceptron (BY-MLP) was used to&#xd;
perform an automatic estimation of SAHS severity (no-SAHS,&#xd;
mild, moderate, and severe), by the use of the selected spectral&#xd;
features from: airflow NPP alone, airflow TH alone, and both&#xd;
sensors jointly. The highest diagnostic performance was&#xd;
reached by BY-MLP only trained with NPP spectral features,&#xd;
reaching Cohen’s  = 0.498 in the overall four-class&#xd;
classification task. It also achieved 91.3%, 84.9%, and 83.3% of&#xd;
accuracy in the binary evaluation of the 3 apnea-hypopnea&#xd;
index cut-offs (5, 15, and 30 events/hour) that define the four&#xd;
SAHS degrees. Our results suggest that TH sensor might be not&#xd;
necessary for SAHS severity estimation if an automatic&#xd;
comprehensive characterization approach is adopted to&#xd;
simplify the diagnostic process</description>
<date>2018-09-03</date>
<date>2018-09-03</date>
<date>2017</date>
<type>info:eu-repo/semantics/conferenceObject</type>
<identifier>http://uvadoc.uva.es/handle/10324/31355</identifier>
<language>eng</language>
<rights>info:eu-repo/semantics/restrictedAccess</rights>
</thesis></metadata></record></GetRecord></OAI-PMH>