• Jayavardhana Gubbi, Ahsan Khandoker, and Marimuthu Palaniswami.
• Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC, 3010, Australia. jgl@unimelb.edu.au
• J Clin Monit Comput. 2012 Feb 1;26(1):1-11.

ObjectiveObstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5 s period.MethodsEight level wavelet packet analysis was performed on each 5 s clip using Daubechies (DB3) mother wavelet and for comparison discrete wavelet analysis was performed using Symlet (SYM3) wavelets. The choice of wavelet basis function was based on a grid search using Daubechies, Symlet and biorthogonal wavelets with decomposition levels varying between 2 and 5. Support vector machine is used for two-class classification. Out of 29 overnight polysomnographic studies, 23 of them were used in the training phase and 6 patients were used for independent testing.ResultsThe proposed algorithm is shown to perform better in classifying CSA and OSA with wavelet packet features (accuracy-91%, sensitivity-88.14% and specificity-91.11%) than with the traditional wavelet decomposition based features (accuracy-83.79%, sensitivity-89.18% and specificity-83.59%). The independent test resulted in overall classification accuracy, sensitivity and specificity of 91.08, 91.02 and 91.09% respectively using wavelet packet analysis.ConclusionsThe classification result indicates the possibility of non-invasively classifying CSA and OSA events based on shorter segments of ECG signals.

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