Key result
Multiscale base-scale entropy analysis successfully distinguished heart rate variability signals among healthy individuals, congestive heart failure patients, and atrial fibrillation patients.
Cross-Sectional (n=79)
Multiscale base-scale entropy is a novel computational method that can successfully distinguish between healthy and pathologic heart rate variability patterns, including atrial fibrillation.
May differentiate cardiac conditions via HRV; hypothesis-generating and requires prospective validation before clinical use.
Multiscale base-scale entropy is introduced in this paper.We use it to analyze heart rate variability series.The results show that multiscale base-scale entropy can identify patterns generated from healthy and pathologic states, and can distinguish daytime and nighttime heartbeat time series. We also calculate the multiscale base-scale entropy of surrogate signal (phase randomized data), compare it with the entropy of atrial fibrillation signal, and find that the tends of two entropys are similar to each other, which indicates that atrial fibrillation reflects the linear characteristics of physiological signals. Multiscale base-scale entropy method has potential applications to studying a wide variety of other physiologic and physical time series data.
No takes yet. Share an insight, caveat, or question.
Bi-ge et al. (2011) conducted a cross-sectional in Congestive heart failure and atrial fibrillation (n=79). Multiscale base-scale entropy analysis vs. Healthy subjects was evaluated on Multiscale base-scale entropy values at different time scales. Multiscale base-scale entropy analysis successfully distinguished heart rate variability signals among healthy individuals, congestive heart failure patients, and atrial fibrillation patients.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: