Please use this identifier to cite or link to this item: https://doi.org/10.1088/0967-3334/32/3/002
Title: Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters
Authors: Acharya, U.R.
Chua, E.C.-P. 
Faust, O.
Lim, T.-C.
Lim, L.F.B.
Keywords: ANN
apnea
correlation dimension
ECG
Hurst exponent
hypopnoea
sleep apnoea
Issue Date: Mar-2011
Citation: Acharya, U.R., Chua, E.C.-P., Faust, O., Lim, T.-C., Lim, L.F.B. (2011-03). Automated detection of sleep apnea from electrocardiogram signals using nonlinear parameters. Physiological Measurement 32 (3) : 287-303. ScholarBank@NUS Repository. https://doi.org/10.1088/0967-3334/32/3/002
Abstract: Sleep apnoea is a very common sleep disorder which can cause symptoms such as daytime sleepiness, irritability and poor concentration. To monitor patients with this sleeping disorder we measured the electrical activity of the heart. The resulting electrocardiography (ECG) signals are both non-stationary and nonlinear. Therefore, we used nonlinear parameters such as approximate entropy, fractal dimension, correlation dimension, largest Lyapunov exponent and Hurst exponent to extract physiological information. This information was used to train an artificial neural network (ANN) classifier to categorize ECG signal segments into one of the following groups: apnoea, hypopnoea and normal breathing. ANN classification tests produced an average classification accuracy of 90%; specificity and sensitivity were 100% and 95%, respectively. We have also proposed unique recurrence plots for the normal, hypopnea and apnea classes. Detecting sleep apnea with this level of accuracy can potentially reduce the need of polysomnography (PSG). This brings advantages to patients, because the proposed system is less cumbersome when compared to PSG. © 2011 Institute of Physics and Engineering in Medicine.
Source Title: Physiological Measurement
URI: http://scholarbank.nus.edu.sg/handle/10635/124672
ISSN: 09673334
DOI: 10.1088/0967-3334/32/3/002
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