Analysis of heart rate variability from 24-hour ambulatory electrocardiographic recordings : significance of preprocessing of R-R interval time series
1University of Oulu, Faculty of Medicine, Institute of Clinical Medicine, Department of Internal Medicine
|Online Access:||PDF Full Text (PDF, 2.5 MB)|
|Persistent link:|| http://urn.fi/urn:isbn:9789514293498
Oulu : University of Oulu,
|Publish Date:|| 2011-01-11
|Thesis type:||Doctoral Dissertation
|Defence Note:||Academic Dissertation to be presented with the assent of the Faculty of Medicine of the University of Oulu for public defence in Auditorium 10 of Oulu University Hospital, on 21 January 2011, at 12 noon
Professor Heikki Huikuri
Professor Tapio Seppänen
Docent Tom Kuusela
Docent Raija Laukkanen
Heart rate variability (HRV) is used in the assessment of cardiovascular health. However, often contradictory results have impeded the efficient use of HRV in clinical practice. HRV signals can contain artifacts leading to errors in the interpretation of HRV results. Various methods have been used for artifact editing, but there is relatively little information on how the actual editing can influence the HRV measures. The main aim of this thesis was to improve the reliability of HRV analysis by concentrating on the HRV signal preprocessing methods.
The effects of three editing methods on the HRV of short (512 R-R) and long-term (24-hour) R-R interval data were studied with non-edited and edited data from healthy subjects (n=10) and patients with acute myocardial infarction (AMI) (n=10). The effects of ectopic beats on short (α1) and long-term (α2) fractal scaling exponents were studied by inserting artificial ectopic beats into the HRV signals of 20 healthy subjects and 20 AMI patients. The prognostic significance of edited and non-edited α1 and α2 was studied in random elderly (n=84) and post-AMI (n=84) populations. A new method to quantify respiratory sinus arrhythmia (RSA) was developed based on the HRV signals of 13 healthy subjects. A new measure, the RSA index, was defined to evaluate the risk to sudden cardiac death (SCD) in 1631 AMI patients. Lastly, a new algorithm was developed in order to edit heart rate (HR) turbulence occurring immediately after a ventricular premature beat (VPB). The effects of HR turbulence editing on the HRV analysis were studied in 267 AMI patients.
Editing had distinct effects on the HRV analysis depending on the editing method and data type. Deletion editing was found to be unsuitable for the HRV spectrum analysis. There was no universal editing method for the time and frequency domain HRV analyses. Unedited ectopic beats increased the randomness of short-term R-R interval dynamics, especially in AMI patients. However, unedited α1 differed significantly between survivors and those who died during the follow-up. Ectopic beats do not necessarily need to be edited if fractal analysis is used in the risk evaluation. A depressed RSA index was found to be a strong predictor of SCD but a weak predictor of non-SCD in AMI patients. Editing of HR turbulence affected differently the various HRV measures. ULF and VLF components were most clearly influenced by HR turbulence removal. The amount of VBPs had an important impact on the results. When the VBPs/hour were >50, ULF and VLF were >30% lower after turbulence removal.
The results of this thesis highlight the importance of editing the erroneous or irrelevant R-R interval oscillation in an HRV analysis. The careful choice of preprocessing method is essential if one wishes to obtain reliable HRV analyses for clinical purposes.
Acta Universitatis Ouluensis. D, Medica
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