Sleep apnea screening by autoregressive models from a single ECG lead
Electrocardiogram; Heart rate variability; Neural network; Pattern classification; Time-varying autoregressive model Auto regressive models; Bivariate; Classification ,; Data sets; Electrocardiogram; Feature subset; Heart rate variability; K-nearest neighbors; k-NN algorithm; Non-invasive way; Obstructive sleep apnea; PhysioNet; QRS complexes; RR intervals; Sequential forward selection; Sleep apnea; Sleep disorders; Spectral feature; Time varying autoregressive model; Training sets; Upper airway; Cardiovascular system; Classifiers; Electrocardiography; Electrochromic devices; Heart valve prostheses; Learning systems; Metal recovery; Neural networks; Power spectral density; Time varying networks; Time varying systems; Sleep research; algorithm; apnea; article; artificial neural network; cardiovascular system; electrocardiography monitoring; heart beat; human; k nearest neighbor; mathematical analysis; QRS complex; RR interval; screening test; sensitivity and specificity; sleep; sleep apnea syndrome; upper respiratory tract; Adult; Algorithms; Computer Simulation; Data Interpretation, Statistical; Diagnosis, Computer-Assisted; Electrocardiography; Female; Humans; Male; Mass Screening; Middle Aged; Models, Biological; Models, Statistical; Regression Analysis; Reproducibility of Results; Sensitivity and Specificity; Sleep Apnea Syndromes
This paper presents a method for obstructive sleep apnea (OSA) screening based on the electrocardiogram (ECG) recording during sleep. OSA is a common sleep disorder produced by repetitive occlusions in the upper airways and this phenomenon can usually be observed also in other peripheral systems such as the cardiovascular system. Then the extraction of ECG characteristics, such as the RR intervals and the area of the QRS complex, is useful to evaluate the sleep apnea in noninvasive way. In the presented analysis, 50 recordings coming from the apnea Physionet database were used; data were split into two sets, the training and the testing set, each of which was composed of 25 recordings. A bivariate time-varying autoregressive model (TVAM) was used to evaluate beat-by-beat power spectral densities for both the RR intervals and the QRS complex areas. Temporal and spectral features were changed on a minute-by-minute basis since apnea annotations where given with this resolution. The training set consisted of 4950 apneic and 7127 nonapneic minutes while the testing set had 4428 apneic and 7927 nonapneic minutes. The K-nearest neighbor (KNN) and neural networks (NN) supervised learning classifiers were employed to classify apnea and non apnea minutes. A sequential forward selection was used to select the best feature subset in a wrapper setting. With ten features the KNN algorithm reached an accuracy of $88\%25$, sensitivity equal to $85\%25$, and specificity up to $90\%25$, while NN reached accuracy equal to $88\%25$, sensitivity equal to $89\%25$ and specificity equal to $86\%25$. In addition to the minute-by-minute classification, the results showed that the two classifiers are able to separate entirely (100%25) the normal recordings from the apneic recordings. Finally, an additional database with eight recordings annotated as normal or apneic was used to test again the classifiers. Also in this new dataset, the results showed a complete separation between apneic and normal recordings. © 2009 IEEE.
Document
Seventh Framework Programme, FP7: 216695
Generically Dependent Continuant
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Article
IEEE Transactions on Biomedical Engineering
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