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Proceedings Paper

Higher-order ambulatory electrocardiogram identification and motion artifact suppression with adaptive second- and third-order Volterra filters
Author(s): Madiha Sabry-Rizk; Walid Zgallai; Sahar El-Khafif; Ewart Carson; Kenneth T. V. Grattan
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Paper Abstract

The objective of this paper is to demonstrate how, in a few seconds, a relatively simple ECG monitor, PC and advanced signal processing algorithms could pinpoint microvolts - late potentials - result from an infarct zone in the heart and is used as an indicator in identifying patients prone to ventricular tachycardia which, if left untreated, leads to ventricular fibrillation. We will characterize recorded ECG data obtained from the standard three vector electrodes during exercise in terms of their higher-order statistical features. Essentially we use adaptive LMS- and Kalman-based second- and third-order Volterra filters to model the non- linear low-frequency P and T waves and motion artifacts which might overlap with the QRS complex and lead to false positive QRS detection. We will illustrate the effectiveness of this new approach by mapping out bispectral regions with a strong bicoherence manifestation and showing their corresponding temporal/spatial origins. Furthermore, we will present a few examples of our own application of these non-invasive techniques to illustrate what we see as their promise for analysis of heart abnormality.

Paper Details

Date Published: 2 October 1998
PDF: 15 pages
Proc. SPIE 3461, Advanced Signal Processing Algorithms, Architectures, and Implementations VIII, (2 October 1998); doi: 10.1117/12.325702
Show Author Affiliations
Madiha Sabry-Rizk, City Univ. (United Kingdom)
Walid Zgallai, City Univ. (United Kingdom)
Sahar El-Khafif, City Univ. (United Kingdom)
Ewart Carson, City Univ. (United Kingdom)
Kenneth T. V. Grattan, City Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 3461:
Advanced Signal Processing Algorithms, Architectures, and Implementations VIII
Franklin T. Luk, Editor(s)

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