Background: Assessment of heart rate variability by means of deceleration capacity (DC) provides a noninvasive probe of cardiac autonomic activity. However, clinical use of DC is limited by the need of manual review of the ECG signals to eliminate artifacts, noise, and nonstationarities.
Objective: To validate a novel approach to fully automatically assess DC from noisy, nonstationary signals
Methods: We analyzed 100 randomly selected ECG tracings recorded for 10 minutes by routine monitor devices (GE DASH 4000, sample size 100 Hz) in a medical emergency department. We used a novel automated R-peak detection algorithm, which is mainly based on a Shannon energy envelope estimator and a Hilbert transformation. We transformed the automatically generated RR interval time series by phase-rectified signal averaging (PRSA) to assess DC of heart rate (DCauto ). DCauto was compared to DCmanual , which was obtained from the same manually preprocessed ECG signals.
Results: DCauto and DCmanual showed good correlation and agreement, particularly if a low-pass filter was implemented into the PRSA algorithm. Correlation coefficient between DCauto and DCmanual was 0.983 (P < 0.0001). Average difference between DCauto and DCmanual was -0.23±0.49 ms with limits of agreement ranging from -1.19 to 0.73 ms. Significantly lower correlations were observed when a different R-peak detection algorithm or conventional heart rate variability (HRV) measures were tested.
Conclusions: DC can be fully automatically assessed from noisy, nonstationary ECG signals.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6932416 | PMC |
| http://dx.doi.org/10.1111/anec.12107 | DOI Listing |
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