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Influence of 2nd-degree AV blocks, ECG recording length, and recording time on heart rate variability analyses in horses


Eggensperger, B H; Schwarzwald, Colin C (2017). Influence of 2nd-degree AV blocks, ECG recording length, and recording time on heart rate variability analyses in horses. Journal of Veterinary Cardiology, 19(2):160-174.

Abstract

OBJECTIVES: To assess the influence of 2nd-degree AV blocks (AVB) on RR interval-based heart rate variability (HRV) variables; to investigate the effect of using PP interval time series and of artifact filtering on HRV analyses; to investigate the influence of electrocardiogram (ECG) recording length and time of recording; and to calculate day-to-day variability and reference intervals of HRV variables.
ANIMALS: Thirty healthy adult horses.
METHODS: RR and PP interval time series were extracted from 10-h Holter ECGs and an automated filter was applied to the RR time series (RRf). Time-domain HRV variables were calculated based on RR, PP, and RRf time series and their relation to the number of AVBs was assessed. Hourly 10- and 60-min segments were extracted to investigate the influence of segment length and recording time on HRV variables. Day-to-day variability and reference intervals of HRV variables were calculated.
RESULTS: Variables of short-term HRV were significantly influenced by the number of AVBs when based on RR, but not when based on PP- and RRf time series. PP- and RRf-based HRV variables were in good agreement. The majority of HRV variables were influenced by recording time and ECG segment length. Day-to-day variability of HRV variables was low when based on 10-h ECG recordings but moderate to high when based on 60-min and 10-min recordings.
CONCLUSIONS: Second-degree AVBs significantly influence conventional RR-based, but not PP- and RRf-based time-domain HRV variables. However, PP and RRf analyses have limitations and recording length and time of recording must be considered.

Abstract

OBJECTIVES: To assess the influence of 2nd-degree AV blocks (AVB) on RR interval-based heart rate variability (HRV) variables; to investigate the effect of using PP interval time series and of artifact filtering on HRV analyses; to investigate the influence of electrocardiogram (ECG) recording length and time of recording; and to calculate day-to-day variability and reference intervals of HRV variables.
ANIMALS: Thirty healthy adult horses.
METHODS: RR and PP interval time series were extracted from 10-h Holter ECGs and an automated filter was applied to the RR time series (RRf). Time-domain HRV variables were calculated based on RR, PP, and RRf time series and their relation to the number of AVBs was assessed. Hourly 10- and 60-min segments were extracted to investigate the influence of segment length and recording time on HRV variables. Day-to-day variability and reference intervals of HRV variables were calculated.
RESULTS: Variables of short-term HRV were significantly influenced by the number of AVBs when based on RR, but not when based on PP- and RRf time series. PP- and RRf-based HRV variables were in good agreement. The majority of HRV variables were influenced by recording time and ECG segment length. Day-to-day variability of HRV variables was low when based on 10-h ECG recordings but moderate to high when based on 60-min and 10-min recordings.
CONCLUSIONS: Second-degree AVBs significantly influence conventional RR-based, but not PP- and RRf-based time-domain HRV variables. However, PP and RRf analyses have limitations and recording length and time of recording must be considered.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:05 Vetsuisse Faculty > Veterinary Clinic > Equine Department
Dewey Decimal Classification:570 Life sciences; biology
630 Agriculture
Uncontrolled Keywords:Automated artifact filtering; PP interval; RR interval; Sinus arrhythmia; Time domain
Language:English
Date:2017
Deposited On:03 Feb 2017 12:43
Last Modified:11 Apr 2017 01:03
Publisher:Elsevier
ISSN:1760-2734
Publisher DOI:https://doi.org/10.1016/j.jvc.2016.10.006
PubMed ID:28117225

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