Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis

• Published in: Physiological measurement Vol. 31; no. 3; pp. 273 - 289
• Main Authors: Mendez, M O, Corthout, J, Van Huffel, S, Matteucci, M, Penzel, T, Cerutti, S, Bianchi, A M
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.03.2010
• Subjects: Adult; Algorithms; Automation; Databases as Topic; Discriminant Analysis; Electrocardiography - methods; Heart - physiopathology; Heart Rate; Humans; Linear Models; Middle Aged; Polysomnography; Respiration; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Sleep - physiology; Sleep Apnea, Obstructive - diagnosis; Sleep Apnea, Obstructive - physiopathology; Time Factors
• ISSN: 0967-3334; 1361-6579; 1361-6579
• DOI: 10.1088/0967-3334/31/3/001

This study analyses two different methods to detect obstructive sleep apnea (OSA) during sleep time based only on the ECG signal. OSA is a common sleep disorder caused by repetitive occlusions of the upper airways, which produces a characteristic pattern on the ECG. ECG features, such as the heart rate variability (HRV) and the QRS peak area, contain information suitable for making a fast, non-invasive and simple screening of sleep apnea. Fifty recordings freely available on Physionet have been included in this analysis, subdivided in a training and in a testing set. We investigated the possibility of using the recently proposed method of empirical mode decomposition (EMD) for this application, comparing the results with the ones obtained through the well-established wavelet analysis (WA). By these decomposition techniques, several features have been extracted from the ECG signal and complemented with a series of standard HRV time domain measures. The best performing feature subset, selected through a sequential feature selection (SFS) method, was used as the input of linear and quadratic discriminant classifiers. In this way we were able to classify the signals on a minute-by-minute basis as apneic or nonapneic with different best-subset sizes, obtaining an accuracy up to 89% with WA and 85% with EMD. Furthermore, 100% correct discrimination of apneic patients from normal subjects was achieved independently of the feature extractor. Finally, the same procedure was repeated by pooling features from standard HRV time domain, EMD and WA together in order to investigate if the two decomposition techniques could provide complementary features. The obtained accuracy was 89%, similarly to the one achieved using only Wavelet analysis as the feature extractor; however, some complementary features in EMD and WA are evident.