Dependence of sleep apnea detection efficiency on the length of ECG recording
Agata Pietrzak , Gerard Cybulski
AbstractOur computer program allows the calculations of commonly accepted six heart rate variability (HRV) parameters in time domain. Those parameters, obtained from long-time one-channel ECG signal recordings, were used for detection of sleep apnea. The classification model was based on the Support Vector Machines (SVM) method using the discriminative Radial Basis Function (RBF) kernel. The aim of study was to check how the length of analyzed single channel ECG overnight recording influences on accuracy of sleep apnea detection.
|Publication size in sheets||0.5|
|Book||Jabłoński Ryszard, Březina Tomáš (eds.): Advanced Mechatronics Solutions, Advances in Intelligent Systems and Computing, 2016, Springer, ISBN 978-3-319-23921-7, 668 p., DOI:10.1007/978-3-319-23923-1|
|Keywords in English||Sleep apnea detection Support Vector Machines ECG respiratory disorders|
|Score|| = 15.0, 05-09-2019, BookChapterSeriesAndMatConfByIndicator|
= 15.0, 05-09-2019, BookChapterSeriesAndMatConfByIndicator
|Publication indicators||= 0; = 0|
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