extract_f_waves: Extract F wave features from ECG

Description

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",
  verbose = TRUE,
  .force_all = FALSE,
  ...
)

Value

A list containing F wave features for each processed lead

Arguments

object: An object of class egm or of subclass ecg
lead: Optional. A character string specifying the lead to analyze. If NULL (default), all available surface 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.
verbose: Logical. If TRUE, print information about which leads will be analyzed. Default is TRUE.
.force_all: Logical. If FALSE (default), only process surface ECG leads. If TRUE, process all available leads. This parameter is ignored if the object is of class 'ecg', in which case all leads are processed.
...: Additional arguments passed to methods

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.