title: Data-dependent filter characteristics of peak-valley respiratory sinus arrhythmia estimation: a cautionary note.
abstract: Filter characteristics of the peak-valley respiratory sinus arrhythmia estimation method are described. To identify filter characteristics of this method, models were generated that combined signals of different frequencies with trends of varying slopes. These models simulate the influence of trend and changing respiratory frequency on the accuracy of peak-valley estimates. The transfer function of the peak-valley method, unlike that of other time domain filters, is not solely dependent upon signal frequency. Two factors interact to determine the relative accuracy of the peak-valley method: (a) slope of the signal component and (b) slope of the underlying trend. Combinations of these factors may result in significant distortion to the input signal. The direction of error is a function of the direction of the trend (i.e., overestimation with deceleration and underestimation with acceleration). In many situations when respiratory sinus arrhythmia amplitude is low in special populations (e.g., cardiovascular disorders, high-risk infants, or human fetuses) or under conditions that greatly reduce respiratory sinus arrhythmia amplitude (e.g., exercise, drugs, pharmacological manipulations), use of the peak-valley method may result in significant measurement error. The use of this method to evaluate respiratory sinus arrhythmia over short epochs (i.e., less than 2 min) or to quantify changes in respiratory sinus arrhythmia due to discrete stimulation (e.g., breath by breath) may result in inconsistent measurement error. Recommendations are made for detrending heart rate data prior to application of the peak-valley method.
authors.affiliation: Institute for Child Study, University of Maryland, College Park 20742.
authors.forename: E A
authors.initials: EA
authors.lastname: Byrne
authors.forename: S W
authors.initials: SW
authors.lastname: Porges
mesh_terms: D001146:Arrhythmia, Sinus / Q000503:physiopathology*; D004562:Electrocardiography / Q000295:instrumentation*; D006801:Humans; D012119:Respiration / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D013997:Time Factors