Robust artefact detection in long-term ECG recordings based on autocorrelation function similarity and percentile analysis.
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.
Department of Electrical Engineering ESAT, SCD-SISTA, and IBBT Future Health Department, Leuven, Belgium. carolina.varon@esat.kuleuven.be
Carolina
C
Varon
Dries
D
Testelmans
Bertien
B
Buyse
Johan A K
JA
Suykens
Sabine
S
Van Huffel
D000465:Algorithms; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000175:diagnosis