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Producción CientíficaHeart rate variability (HRV) provides useful information about heart dynamics
both under healthy and pathological conditions. Entropy measures have shown their utility
to characterize these dynamics. In this paper, we assess the ability of spectral entropy (SE)
and multiscale entropy (MsE) to characterize the sleep apnoea-hypopnea syndrome (SAHS)
in HRV recordings from 188 subjects. Additionally, we evaluate eventual differences in
these analyses depending on the gender. We found that the SE computed from the very low
frequency band and the low frequency band showed ability to characterize SAHS regardless
the gender; and that MsE features may be able to distinguish gender specificities. SE and
MsE showed complementarity to detect SAHS, since several features from both analyses
were automatically selected by the forward-selection backward-elimination algorithm.
Finally, SAHS was modelled through logistic regression (LR) by using optimum sets of
selected features. Modelling SAHS by genders reached significant higher performance than
doing it in a jointly way. The highest diagnostic ability was reached by modelling SAHS in
women. The LR classifier achieved 85.2% accuracy (Acc) and 0.951 area under the ROC
curve (AROC). LR for men reached 77.6% Acc and 0.895 AROC, whereas LR for the whole set reached 72.3% Acc and 0.885 AROC. Our results show the usefulness of the SE and MsE
analyses of HRV to detect SAHS, as well as suggest that, when using HRV, SAHS may be
more accurately modelled if data are separated by gender.Ministerio de Economía, Industria y Competitividad (TEC2011-22987)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA059U13
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