Key result
ECG complexity measures successfully track and visualize dynamic changes in fibrillation patterns over time.
Why the study?
A frequent observation during cardiac fibrillation is a fluctuation in complexity with irregular patterns interrupted by more regular phases of varying length, requiring quantification methods.
Applying complexity measures to raw ECG signals can detect intermittent complexity variations during cardiac fibrillation, which may aid in diagnosis and treatment.
Complexity measures on raw ECG detect fibrillation variations; hypothesis-generating for monitoring, requires prospective validation.
OBJECTIVE: A frequent observation during cardiac fibrillation is a fluctuation in complexity where the irregular pattern of the fibrillation is interrupted by more regular phases of varying length. APPROACH: We apply different measures to sliding windows of raw ECG signals for quantifying the temporal complexity. The methods include permutation entropy, power spectral entropy, a measure for the extent of the set of reconstructed states and several wavelet measures. MAIN RESULTS: Using these methods, variations of fibrillation patterns over time are detected and visualized. SIGNIFICANCE: These quantifications can be used to characterize different phases of the ECG during fibrillation and might improve diagnosis and treatment methods for heart diseases.
No takes yet. Share an insight, caveat, or question.
Schlemmer et al. (2017) studied Cardiac fibrillation. Complexity measures (permutation entropy, power spectral entropy, wavelet measures) was evaluated on Detection and visualization of variations of fibrillation patterns over time. Application of complexity measures to raw ECG signals successfully detected and visualized variations in fibrillation patterns over time.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: