In the sleep apnea-hypopnea syndrome (SAHS)
context, airflow signal plays a key role for the simplification of
the diagnostic process. It is measured during the standard
diagnostic test by the acquisition of two simultaneous sensors: a
nasal prong pressure (NPP) and a thermistor (TH). The
current study focuses on the comparison of their spectral
content to help in the automatic SAHS-severity estimation. The
spectral analysis of 315 NPP and corresponding TH recordings
is firstly proposed to characterize the conventional band of
interest for SAHS (0.025-0.050 Hz.). A magnitude squared
coherence analysis is also conducted to quantify possible
differences in the frequency components of airflow from both
sensors. Then, a feature selection stage is implemented to assess
the relevance and redundancy of the information extracted
from the spectrum of NPP and TH airflow. Finally, a multiclass
Bayesian multi-layer perceptron (BY-MLP) was used to
perform an automatic estimation of SAHS severity (no-SAHS,
mild, moderate, and severe), by the use of the selected spectral
features from: airflow NPP alone, airflow TH alone, and both
sensors jointly. The highest diagnostic performance was
reached by BY-MLP only trained with NPP spectral features,
reaching Cohen’s = 0.498 in the overall four-class
classification task. It also achieved 91.3%, 84.9%, and 83.3% of
accuracy in the binary evaluation of the 3 apnea-hypopnea
index cut-offs (5, 15, and 30 events/hour) that define the four
SAHS degrees. Our results suggest that TH sensor might be not
necessary for SAHS severity estimation if an automatic
comprehensive characterization approach is adopted to
simplify the diagnostic process
