Automated assessment of cardiac autonomic function by means of deceleration capacity from noisy, nonstationary ECG signals: validation study.

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.