The sleep apnea-hypopnea syndrome (SAHS) is a
chronic respiratory disorder of high prevalence among children
(up to 4%). Nocturnal polysomnography (PSG) is the gold
standard method to diagnose SAHS, which is a complex,
expensive, and time-consuming test. Consequently, alternative
simplified methods are demanded. We propose the analysis of
the respiratory rate variability (RRV) signal, directly obtained
from the airflow (AF) signals. The aim of our study is to
evaluate the usefulness of the spectral information obtained
from RRV in the diagnosis of pediatric SAHS. A database
composed of 946 AF and blood oxygen saturation (SpO2)
recordings from children between 0 and 13 years old was used.
Our database was divided into four severity groups according
to the apnea-hipopnea index (AHI): no-SAHS (AHI < 1
events/h), mild (1 events/h ≤ AHI < 5 events/h), moderate (5
events/h ≤ AHI < 10 events/h), and severe SAHS (AHI ≥ 10
events/h). RRV and 3% oxygen desaturation index (ODI3) were
obtained from AF and SpO2 recordings, respectively. A
spectral band of interest was determined (0.09–0.20 Hz.) and a
total of 12 spectral features were extracted. Nine of these
features showed statistically significant differences (p-value <
0.05) among the four severity groups. The spectral features
from RRV along with ODI3 were used as inputs to binary
logistic regression (LR) classifiers. The diagnostic performance
of LR models were evaluated for the AHI cut-off points of 1, 5,
and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy,
respectively. These results outperformed those obtained by
single ODI3. The joint use of the spectral information from
RRV and ODI3 achieved a high diagnostic capability in the
most severely-affected children, thus showing their
complementarity. These results suggest that the information
contained in RRV spectrum together with ODI3 is useful to help
identify moderate-to-severe SAHS.
