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Advisor(s)
Abstract(s)
A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for
many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as
the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability
(HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark
with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM),
among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity
of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection
may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler
alternative to classical solutions for the sleep apnea detection, for example the ones based on the
Polysomnography.
Description
Keywords
Sleep apnea Electrocardiogram (ECG) Heart rate variability (HRV) ECG-derived respiration (EDR) Feature selection Classification Artificial neural network (ANN) Support vector machine (SVM) Linear discriminant analysis (LDA) Partial least squares regression (PLS) Regression analysis (REG) Wiener–Hopf equation (wienerHopf) Augmented naive bayesian classifier (aNBC) Perceptron learning algorithm