Comparison of Atrial Signal Extraction Algorithms in 12-Lead ECGs With Atrial Fibrillation

Abstract

Analysis of atrial rhythm is important in the treatment and management of patients with atrial fibrillation. Several algorithms exist for extracting the atrial signal from the electrocardiogram (ECG) in atrial fibrillation, but there are few reports on how well these techniques are able to recover the atrial signal. We assessed and compared three algorithms for extracting the atrial signal from the 12-lead ECG. The 12-lead ECGs of 30 patients in atrial fibrillation were analyzed. Atrial activity was extracted by three algorithms, Spatiotemporal QRST cancellation (STC), principal component analysis (PCA), and independent component analysis (ICA). The amplitude and frequency characteristics of the extracted atrial signals were compared between algorithms and against reference data. Mean (standard deviation) amplitude of QRST segments of V1 was 0.99 (0.54) mV, compared to 0.18 (0.11) mV (STC), 0.19 (0.13) mV (PCA), and 0.29 (0.22) mV (ICA). Hence, for all algorithms there were significant reductions in the amplitude of the ventricular activity compared with that in V1. Reference atrial signal amplitude in V1 was 0.18 (0.11) mV, compared to 0.17 (0.10) mV (STC), 0.12 (0.09) mV (PCA), and 0.18 (0.13) mV (ICA) in the extracted atrial signals. PCA tended to attenuate the atrial signal in these segments. There were no significant differences for any of the algorithms when comparing the amplitude of the reference atrial signal with that of the extracted atrial signals in segments in which ventricular activity had been removed. There were no significant differences between algorithms in the frequency characteristics of the extracted atrial signals. There were discrepancies in amplitude and frequency characteristics of the atrial signal in only a few cases resulting from notable residual ventricular activity for PCA and ICA algorithms. In conclusion, the extracted atrial signals from these algorithms exhibit very similar amplitude and frequency characteristics. Users of these algorithms should be observant of residual ventricular activities which can affect the analysis of the fibrillatory waveform in clinical practice.

Applications of Signal Analysis to Atrial Fibrillation

“…They can be based on principal component analysis (PCA) [18] or blind source separation (BSS) [4]. These methodologies have been compared in a joint study proving their coincident results in the estimation of AF spectra on the surface ECG [19]. One common drawback to the ABS-based methods is that they are mainly thought to be applied over single lead ECGs.…”

ECG signal quantization effects in the analysis of atrial fibrillation

“…Furthermore, time-domain methods have been successfully applied in the characterization of AF episodes using the surface electrocardiogram (ECG) [3,4]. The exhaustive analysis of AF episodes requires previously to separate the atrial activity (AA) component from other bioelectric signals that contribute to the ECG formation [5]. Given that the AA and the ventricular activity (VA) overlap spectrally, linear filtering cannot be use to separate them.…”

Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

“…An accurate extraction of the AA from AF ECG recordings is the base for a subsequent precise and useful analysis which can help to the clinical management of the arrhythmia [9]. The management of patients with AF requires the knowledge of its pattern of presentation (paroxysmal, persistent, or permanent) and the underlying conditions in order to take decisions about restoration or maintenance of sinus rhythm, control of the ventricular rate, and antithrombotic therapy [8].…”

“…The reason for seeking an accurate AA extraction is its medical applicability, since it is accepted that noninvasive measurement of fibrillatory waves is useful for the treatment and management of patients [9,42]. Once the AA is obtained with enough accuracy from ECG recordings, it can be used for the characterization of PAF episodes by signal analysis.…”

“…9. CBSS algorithms mean SIR AA in the first environment as a function of Nm regardless of Ns expressed with one significant decimal.…”