Extract F wave features from ECG — extract_f

This function analyzes F waves in an ECG signal, extracting various characteristics.

Usage

extract_f_waves(
  object,
  lead = NULL,
  qrs_method = "adaptive_svd",
  f_characteristics = "amplitude",
  ...
)

Arguments

object: An object of class egm
lead: Optional. A character string specifying the lead to analyze. If NULL (default), all available leads will be processed.
qrs_method: Method for ventricular signal removal. Default is "adaptive_svd" for adaptive singular value decomposition.
f_characteristics: Vector of characteristics to analyze from ECG signal. Options: "amplitude", "approximate_entropy", "dominant_frequency". Please see calculate_approximate_entropy() and calculate_dominant_frequency() for more details.
...: Additional arguments passed to methods

Value

A list containing F wave features for each processed lead

References

Park, Junbeom, Chungkeun Lee, Eran Leshem, Ira Blau, Sungsoo Kim, Jung Myung Lee, Jung-A Hwang, Byung-il Choi, Moon-Hyoung Lee, and Hye Jin Hwang. “Early Differentiation of Long-Standing Persistent Atrial Fibrillation Using the Characteristics of Fibrillatory Waves in Surface ECG Multi-Leads.” Scientific Reports 9 (February 26, 2019): 2746. https://doi.org/10.1038/s41598-019-38928-6.

Hyvarinen, A., and Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430.