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Updated: May 14, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
Published on: May 23, 2021
Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile
Carolina Varon1, Dries Testelmans, Bertien Buyse
1Department of Electrical Engineering ESAT, SCD-SISTA, and IBBT Future Health Department, Leuven, Belgium. carolina.varon@esat.kuleuven.be
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.
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