Open AccessAutomated Assessment of Cardiac Autonomic Function by Means of Deceleration Capacity from Noisy, Nonstationary ECG Signals: Validation Study
Author(s) -
Eick Christian,
Rizas Konstantinos D.,
Zuern Christine S.,
Bauer Axel
Publication year - 2014
Publication title -
annals of noninvasive electrocardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.494
H-Index - 48
eISSN - 1542-474X
pISSN - 1082-720X
DOI - 10.1111/anec.12107
Subject(s) - medicine , heart rate variability , estimator , signal (programming language) , autonomic function , heart rate , pattern recognition (psychology) , algorithm , artificial intelligence , statistics , computer science , mathematics , blood pressure , programming language
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 (DC auto ). DC auto was compared to DC manual , which was obtained from the same manually preprocessed ECG signals. Results DC auto and DC manual showed good correlation and agreement, particularly if a low‐pass filter was implemented into the PRSA algorithm. Correlation coefficient between DC auto and DC manual was 0.983 (P < 0.0001). Average difference between DC auto and DC manual 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.