ECG-Derived Respiratory Rate in Atrial Fibrillation

IEEE Trans Biomed Eng. 2020 Mar;67(3):905-914. doi: 10.1109/TBME.2019.2923587. Epub 2019 Jun 18.

Abstract

Objective: The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated.

Methods: The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal- and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals.

Results: Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015 ± 0.021 Hz and 0.019 ± 0.025 Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034 ± 0.039 Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA.

Conclusion: The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed.

Significance: The respiratory rate can be robustly estimated from the ECG in the presence of AF.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Atrial Fibrillation / diagnosis*
  • Atrial Fibrillation / physiopathology*
  • Electrocardiography / methods*
  • Female
  • Humans
  • Male
  • Respiratory Rate / physiology*
  • Signal Processing, Computer-Assisted*
  • Signal-To-Noise Ratio