Detection of obstructive sleep apnea by empirical mode decomposition on tachogram

In this study we proposed a method for screening the obstructive apnea in an easy and non-invasive way by following the tachogram. Ensemble Empirical Mode Decomposition (EEMD), as proposed in [6] is applied, Intrinsic Mode Functions (IMF) are derived and amplitudes and frequencies from different modes are extracted. Frequencies are computed by applying a new Generalized Zero Crossing method on IMF as proposed by Huang [7]. Once frequencies and amplitudes of different modes have been extracted, the optimal performing subset of features has been derived. Linear discriminant analysis (LDA) was used to classify apneic events on a minute-by-minute basis. Our algorithm showed fairly good performance with sensitivity of nearly 89%25 and accuracy higher than 83%25 which is at least comparable with the best performing methods. However, the set of features used in this work was significantly smaller than in other studies, e.g. Corthout et al. [5]. In this study only EMD-based methods are used in order to show the power of that family of algorithms, and only the tachogram is processed, so the computational power of the algorithm is enhanced. © 2009 Springer Berlin Heidelberg.

Automatic Detection; Empirical Mode Decomposition; Heart Rate Variability; Obstructive Sleep Apnea Automatic Detection; Computational power; Different modes; Empirical Mode Decomposition; Ensemble empirical mode decomposition; Heart Rate Variability; Intrinsic mode functions; Linear discriminant analysis; Non-invasive way; Obstructive apnea; Obstructive sleep apnea; Zero crossing methods; Algorithms; Discriminant analysis; Heart; Power quality; Sleep research

VIVO