New automated detection method of OSA based on artificial neural networks using P-wave shape and time changes - Lweesy, Khaldon; Fraiwan, Luay; Khasawneh, Natheer; Dickhaus, Hartmut (Journal of medical systems, 2011)
This paper describes a new method for automatic detection of obstructive sleep apnea (OSA) based on artificial neural networks (ANN) using regular electrocardiogram (ECG) recordings. ECG signals were pre-processed and segmented to extract the P-waves; then three P-wave features were extracted: the P-wave duration (Tp), the P-wave dispersion (Pd), and the time interval from the peak of the P-wave to the R-wave (Tpr). Combinations of the three features were used as features for classification using ANN. For each feature combination studied, 70% of the input data was used for training the ANN, 15% for validating, and 15% for testing the results. Perfect agreement between expert’s scores and the ANN scores was achieved when the ANN was applied on Tp, Pd, and Tprtaken together, while substantial agreements were achieved when applying the ANN on the feature combinations Tpand Pd, and Tpand Tpr.
Published online: 12 December 2009
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