New automated detection method of OSA based on artificial neural networks using P-wave shape and time changes

• Verfasst von: Lweesy, Khaldon [VerfasserIn]
• Verfasst von: Fraiwan, Luay [VerfasserIn]
• Verfasst von: Khasawneh, Natheer [VerfasserIn]
• Verfasst von: Dickhaus, Hartmut [VerfasserIn]
• Titel: New automated detection method of OSA based on artificial neural networks using P-wave shape and time changes
• Verf.angabe: Khaldon Lweesy, Luay Fraiwan, Natheer Khasawneh, Hartmut Dickhaus
• Jahr: 2011
• Umfang: 12 S.
• Fussnoten: Published online: 12 December 2009 ; Gesehen am 22.11.2022
• Titel Quelle: Enthalten in: Journal of medical systems
• Ort Quelle: New York, NY : Plenum Press, 1977
• Jahr Quelle: 2011
• Band/Heft Quelle: 35(2011), 4, Seite 723-734
• ISSN Quelle: 1573-689X
• DOI: doi:10.1007/s10916-009-9409-z
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Artificial neural network (ANN)
• Sach-SW: Automated detection of OSA
• Sach-SW: Obstructive sleep apnea (OSA)
• Sach-SW: P-wave
• K10plus-PPN: 182319043X

Abstract

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.