Abstract
While epidemiological data support the link between reduced heart rate variability (HRV) and a multitude of pathologies, the mechanisms underlying changes in HRV and disease progression are poorly understood. Even though we have numerous rodent models of disease for mechanistic studies, not being able to reliably measure HRV in conscious, freely moving rodents has hindered our ability to extrapolate the role of HRV in the progression from normal physiology to pathology. The sheer number of heart beats per day (>800,000 in mice) makes data exclusion both time consuming and daunting. We sought to evaluate an RR interval exclusion method based on percent (%) change of adjacent RR intervals. Two approaches were evaluated: % change from “either” and “both” adjacent RR intervals. The data exclusion method based on standard deviation (SD) was also evaluated for comparison. Receiver operating characteristic (ROC) curves were generated to determine the performance of each method. Results showed that exclusion based on % change from “either” adjacent RR intervals was the most accurate method in identifying normal and abnormal RR intervals, with an overall accuracy of 0.92–0.99. As the exclusion value increased (% change or SD), the sensitivity (correctly including normal RR intervals) increased exponentially while the specificity (correctly rejecting abnormal RR intervals) decreased linearly. Compared to the SD method, the “either” approach had a steeper rise in sensitivity and a more gradual decrease in specificity. The intersection of sensitivity and specificity where the exclusion criterion had the same accuracy in identifying normal and abnormal RR intervals was 10–20% change for the “either” approach and ∼ 1 SD for the SD-based exclusion method. Graphically (tachogram and Lorenz plot), 20% change from either adjacent RR interval resembled the data after manual exclusion. Finally, overall (SDNN) and short-term (rMSSD) indices of HRV generated using 20% change from “either” adjacent RR intervals as the exclusion criterion were closer to the manual exclusion method with lower subject-to-subject variability than those generated using the 2 SD exclusion criterion. Thus, 20% change from “either” adjacent RR intervals is a good criterion for data exclusion for reliable 24-h time domain HRV analysis in rodents.
Highlights
MATERIALS AND METHODSThe concept of using respiration-related changes in heart beat as a marker for cardiac vagal regulation was first introduced four decades ago (Katona and Jih, 1975; Hedman et al, 1995)
This is true for recordings from mice, in part due to their size; changes in posture and activity can profoundly alter the relative position of the two electrocardiogram (ECG) leads, which alters the quality of ECG signals, as well as introduces electromyography noise or creates non-physiological noise from lead movements
Some of the longer intervals were outside of the 95% confidence interval and will be excluded using the 2 standard deviation (SD) exclusion criterion
Summary
MATERIALS AND METHODSThe concept of using respiration-related changes in heart beat (respiratory sinus arrhythmia) as a marker for cardiac vagal regulation was first introduced four decades ago (Katona and Jih, 1975; Hedman et al, 1995). With longer recording periods and/or recordings from conscious and freely moving subjects, abnormal beats and artifacts become common in the data set, which makes identifying abnormal RR intervals a challenging task. This is true for recordings from mice, in part due to their size; changes in posture and activity can profoundly alter the relative position of the two electrocardiogram (ECG) leads, which alters the quality of ECG signals, as well as introduces electromyography noise or creates non-physiological noise from lead movements. Based on our previous studies, on average, a 24-h ECG recording from a mouse requires approximately 1 week to visually inspect all RR intervals and manually exclude abnormal RR intervals (Chen et al, 2008; Pham et al, 2009)
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