Baseline wander removal is an unavoidable step in ECG signal processing. The in-band nature of this noise makes its removal difficult without affecting the ECG, in particular the ST segment. This portion of the ECG has high clinical relevance, as it is related to the diagnosis of acute coronary syndromes. We have recently proposed a novel approach to baseline wander removal based on the notion of quadratic variation reduction. In this paper, we assess its performance in terms of both effectiveness in removing baseline wander and distortion introduced in the ST segment. Numerical results highlight the effectiveness of the approach, which outperforms state-of-the-art algorithms both in removing baseline drift and preserving the ST segment. The algorithm is also very fast, as its computational complexity is linear in the size of the vector to detrend.
ECG Baseline Wander Removal by QVR Preserving the ST Segment / Fasano, A; Villani, Valeria. - (2014), pp. 117-118. (Intervento presentato al convegno 2014 8th Conference of the European Study Group on Cardiovascular Oscillations, ESGCO 2014 tenutosi a Trento, ita nel May 25-28, 2014) [10.1109/ESGCO.2014.6847547].
ECG Baseline Wander Removal by QVR Preserving the ST Segment
VILLANI, VALERIA
2014
Abstract
Baseline wander removal is an unavoidable step in ECG signal processing. The in-band nature of this noise makes its removal difficult without affecting the ECG, in particular the ST segment. This portion of the ECG has high clinical relevance, as it is related to the diagnosis of acute coronary syndromes. We have recently proposed a novel approach to baseline wander removal based on the notion of quadratic variation reduction. In this paper, we assess its performance in terms of both effectiveness in removing baseline wander and distortion introduced in the ST segment. Numerical results highlight the effectiveness of the approach, which outperforms state-of-the-art algorithms both in removing baseline drift and preserving the ST segment. The algorithm is also very fast, as its computational complexity is linear in the size of the vector to detrend.Pubblicazioni consigliate
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