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

Author

Varon, Carolina ; Testelmans, D. ; Buyse, B. ; Suykens, Johan A. K. ; Van Huffel, Sabine

Author_Institution

Dept. of Electr. Eng. ESAT, KU Leuven, Leuven, Belgium

fYear

2012

fDate

Aug. 28 2012-Sept. 1 2012

Firstpage

3151

Lastpage

3154

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.

Keywords

diseases; electrocardiography; graph theory; medical signal detection; medical signal processing; neurophysiology; signal classification; ECG; autocorrelation function similarity; easy-to-implement tools; electrocardiogram; graph theory; percentile analysis; robust artefact detection; sleep apnea classification; Algorithm design and analysis; Clustering algorithms; Correlation; Electrocardiography; Fourier transforms; Sensitivity; Sleep apnea; Algorithms; Electrocardiography; Humans; Signal Processing, Computer-Assisted; Sleep Apnea Syndromes;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE

Conference_Location

San Diego, CA

ISSN

1557-170X

Print_ISBN

978-1-4244-4119-8

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/EMBC.2012.6346633

Filename

6346633