Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis

Carolina Varon; Dries Testelmans; Bertien Buyse; Johan A K Suykens; Sabine Van Huffel

doi:10.1109/EMBC.2012.6346633

Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis

Annu Int Conf IEEE Eng Med Biol Soc. 2012:2012:3151-4. doi: 10.1109/EMBC.2012.6346633.

Authors

Carolina Varon¹, Dries Testelmans, Bertien Buyse, Johan A K Suykens, Sabine Van Huffel

Affiliation

¹ Department of Electrical Engineering ESAT, SCD-SISTA, and IBBT Future Health Department, Leuven, Belgium. [email protected]

PMID: 23366594
DOI: 10.1109/EMBC.2012.6346633

Abstract

Artefacts can pose a big problem in the analysis of electrocardiogram (ECG) signals. Even though methods exist to reduce the influence of these contaminants, they are not always robust. In this work a new algorithm based on easy-to-implement tools such as autocorrelation functions, graph theory and percentile analysis is proposed. This new methodology successfully detects corrupted segments in the signal, and it can be applied to real-life problems such as for example to sleep apnea classification.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Electrocardiography / methods*
Humans
Signal Processing, Computer-Assisted
Sleep Apnea Syndromes / diagnosis