CARACAS, a novel automated tool for Cardiac Artifact Removal in Absence of CArdiac Signal

Abstract

BackgroundEEG recordings can contain cardiac related artifacts. Independent Component Analysis (ICA) followed by removal of cardiac Independent Components (ICs) is a powerful and widely used strategy for artifact correction. Most existing methods for automatic labeling of cardiac ICs require a simultaneously recorded ECG (e.g., to compute correlation with the IC time course). However, ECG is not always available. To address this limitation, we developed CARACAS (Cardiac Artifact Removal in Absence of CArdiac Signal), a novel tool that identifies cardiac ICs using only the IC time courses. New methodBecause cardiac ICs exhibit temporal profiles highly similar to ECG signals, we used an existing tool designed to detect cardiac events (R waves) in ECG signals and applied it to each IC time course. Analysis of the detected events enabled the differentiation of cardiac ICs from non-cardiac ICs, where unrelated signal variations are incorrectly identified as cardiac events. Using the 375 EEG-ECG recordings of the open-source dataset OpenNeuro ds003690, we compared the performances of three algorithms: CARACAS, IClabel (a generic IC classifier which does not require ECG), and correlation with ECG channel. Results (comparison with existing methods)A total of 21,375 ICs were manually and automatically classified. CARACAS achieved high performance (sensitivity = 0.960, specificity = 0.976), substantially outperforming ICLabel (sensitivity = 0.210, specificity = 0.999) and approaching the performance of ECG correlation method (sensitivity = 0.975, specificity = 0.998). ConclusionWe present a reliable ECG-free algorithm for cardiac IC detection in EEG. CARACAS provides a practical solution when ECG is unavailable, and is implemented in the SASICA toolbox. HighlightsO_LICardiac independent component (IC) removal after ICA corrects cardiac EEG artifacts. C_LIO_LIMost automatic cardiac IC detectors require a simultaneously recorded ECG. C_LIO_LIWe developed CARACAS, a novel ECG-free method for automatic cardiac IC labeling. C_LIO_LICARACAS achieved a sensitivity of 0.960 and a specificity of 0.976 on 21,375 ICs. C_LIO_LICARACAS outperforms ICLabel, and is available in SASICA toolbox (command line & GUI). C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=148 SRC="FIGDIR/small/673728v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@178bd49org.highwire.dtl.DTLVardef@1d34500org.highwire.dtl.DTLVardef@1572bf1org.highwire.dtl.DTLVardef@5f799_HPS_FORMAT_FIGEXP M_FIG C_FIG