Open AccessQRS residual removal in atrial activity signals extracted from single lead: a new perspective based on signal extrapolation
Author(s) -
Dai Huhe,
Yin Liyan,
Li Ye
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2015.0508
Subject(s) - qrs complex , extrapolation , residual , signal (programming language) , atrial fibrillation , subtraction , computer science , electrocardiography , pattern recognition (psychology) , mathematics , algorithm , artificial intelligence , cardiology , medicine , statistics , arithmetic , programming language
Atrial activity (AA) signal must first be extracted from atrial fibrillation electrocardiogram (AF ECG) before it is used to characterise AF. However, extracting AA signal is not an easy task, especially from single‐lead ECG recording. The AA signals within QRS intervals extracted by the existing single‐lead extraction methods are often heavily distorted due to the existence of large QRS residuals. In this study, the authors focus on reducing the QRS residuals in the extracted AA signals, and propose a novel signal extrapolation based method. AA signal is assumed to be band‐limited, and a dedicated extrapolation formula is derived. Based on this extrapolation formula, the AA samples within QRS interval are reconstructed by using the ones in the adjacent SQ segments. The experiments with simulated AF ECGs showed that, after using the proposed method, the normalised mean square error of the AA signal extracted by average beat subtraction method decreased by 26–50%, 15–36%, 12–40 and 42–63% for simulated AF ECGs in lead I, II, V 1 and V 6 , respectively. Experiments with real AF ECG also proved that the proposed method is able to greatly reduce the ventricular residuals of the extracted AA signal.