4th European Conference of the International Federation for Medical and Biological Engineering pp 247-251 | Cite as
Detection of Obstructive Sleep Apnea by Empirical Mode Decomposition on Tachogram
Abstract
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% and accuracy higher than 83% 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.
Keywords
Empirical Mode Decomposition Obstructive Sleep Apnea Heart Rate Variability Automatic DetectionPreview
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