Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

Abstract

The autonomic nervous system (ANS) is an important factor in cardiac arrhythmia, and information about ANS activity during atrial fibrillation (AF) may contribute to personalized treatment. In this study we aim to quantify respiratory modulation in the f-wave frequency trend from resting ECG. First, an f-wave signal is extracted from the ECG by QRST cancelation. Second, an f-wave model is fitted to the f-wave signal to obtain a high resolution f-wave frequency trend and an index for signal quality control ( $S$ ). Third, respiratory modulation in the f-wave frequency trend is extracted by applying a narrow band-pass filter. The center frequency of the band-pass filter is determined by the respiration rate. Respiration rate is estimated from a surrogate respiration signal, obtained from the ECG using homomorphic filtering. Peak conditioned spectral averaging, where spectra of sufficient quality from different leads are averaged, is employed to obtain a robust estimate of the respiration rate. The envelope of the filtered f-wave frequency trend is used to quantify the magnitude of respiratory induced f-wave frequency modulation. The proposed methodology is evaluated using simulated f-wave signals obtained using a sinusoidal harmonic model. Results from simulated signals show that the magnitude of the respiratory modulation is accurately estimated, quantified by an error below 0.01 Hz, if the signal quality is sufficient ( $S>0.5$ ). The proposed method was applied to analyze ECG data from eight pacemaker patients with permanent AF recorded at baseline, during controlled respiration, and during controlled respiration after injection of atropine, respectively. The magnitude of the respiratory induce f-wave frequency modulation was 0.15 ± 0.01, 0.18 ± 0.02, and 0.17 ± 0.03 Hz during baseline, controlled respiration, and post-atropine, respectively. Our results suggest that parasympathetic regulation affects the magnitude of respiratory induced f-wave frequency modulation.

Keywords: ECG processing; atrial fibrillation; autonomic nervous system; f-wave frequency; parasympathetic regulation; respiratory modulation.

Figure 1

Schematic of the proposed method.

Figure 2

Example of the x_sim(n) with f = 5 Hz, f_r = 0.2 Hz, Δf = 0.1 Hz, and (A) σ_Φ= 0, (B) σ_Φ= 0.25, (C) σ_Φ= 0.5, (D) σ_Φ= 0.75, (E) σ_Φ=1.

Figure 3

Left subplots indicate r_l(n) from lead V1, V2, V3, V4, V5, V6, I, II, and III during baseline in the patient (b), and middle subplots are corresponding Welch periodogram. The prominent peak in the respiratory interval (red solid line) is shown with red marker. The averaged spectra and respiration rate estimate can be seen in the right subplot.

Figure 4

Signal quality $\mathcal{S}$ of x_sim(n) plotted vs. σ_Φ. Red dots indicate the mean and blue whiskers indicate the std of $\mathcal{S}$.

Figure 5

Estimation error $|\Delta \overline{f}$-Δf| and corresponding signal quality $\mathcal{S}$ of x_sim(n).

Figure 6

Signals obtained in each step of the analysis of patient b in phase B. (A) ECG from lead V1, (B) corresponding extracted f-waves x(n), (C) estimated f-wave frequency trend $\widehat{f}(n)$, and (D) corresponding (solid blue) filtered $\widehat{f}(n)$, (dashed purple) $\Delta \widehat{f}(n)$, and (solid red) estimated $\Delta \overline{f}$. Note the that (A,B) shows 10 s excerpts of the signals, whereas (C,D) shows the full 5 min signal.

Figure 7

$\Delta \overline{f}$ estimated from phase B, CR, and PA recordings, respectively. Each curve corresponds to a patient.

Figure 8

The modulation magnitude $\Delta \overline{f}$ plotted vs. average the f-wave frequency $\overline{f}$ for each patient during (top) B, (middle) CR, and (bottom) PA phase, respectively. Patients are identified with different colors.