New automated detection method of OSA based on artificial neural networks using P-wave shape and time changes.
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 ).
Lweesy
Khaldon
K
Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid, 22110, Jordan. klweesy@just.edu.jo
Fraiwan
Luay
L
Khasawneh
Natheer
N
Dickhaus
Hartmut
H
D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology