New Automated Detection Method of OSA Based on Artificial Neural Networks Using P-Wave Shape and Time Changes
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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 (T p ), the P-wave dispersion (P d ), and the time interval from the peak of the P-wave to the R-wave (T pr ). 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 T p , P d , and T pr taken together, while substantial agreements were achieved when applying the ANN on the feature combinations T p and P d , and T p and T pr .