Corrected Integral Shape Averaging Applied to Obstructive Sleep Apnea Detection from the Electrocardiogram. (11th January 2007)

• Record Type: Journal Article
• Authors: Boudaoud Boudaoud, S. S.; Rix Rix, H. H.; Meste Meste, O. O.; Heneghan Heneghan, C. C.; O'Brien O'Brien, C. C.
• Other Names: Sandham Sandham William Allan William Allan Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2007-01-11
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/32570
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : We present a technique called corrected integral shape averaging (CISA) for quantifying shape and shape differences in a set of signals. CISA can be used to account for signal differences which are purely due to affine time warping (jitter and dilation/compression), and hence provide access to intrinsic shape fluctuations. CISA can also be used to define a distance between shapes which has useful mathematical properties; a mean shape signal for a set of signals can be defined, which minimizes the sum of squared shape distances of the set from the mean. The CISA procedure also allows joint estimation of the affine time parameters. Numerical simulations are presented to support the algorithm for obtaining the CISA mean and parameters. Since CISA provides a well-defined shape distance, it can be used in shape clustering applications based on distance measures such ask -means. We present an application in which CISA shape clustering is applied to P-waves extracted from the electrocardiogram of subjects suffering from sleep apnea. The resulting shape clustering distinguishes ECG segments recorded during apnea from those recorded during normal breathing with a sensitivity of81 % and specificity of84 % .