Analyzing multiresolution wavelet entropy of ECG with visual analytics techniques

Wavelet entropy is an analysis tool applicable to biomedical signals, including EEG and ECG. The current paper proposes an application of methods from visual analytics to time-frequency energy distribution in ECG signals, with the goal of studying heart rate variability from the main ECG band corresponding to heart rate. Such an approach can complement standard time- and frequency-domain techniques for heart rate variability assessment. Multiresolution wavelet entropy was computed from continuous wavelet transforms using windows of user-specified length and overlap. An interactive tool provides visualization of multiresolution entropy heat maps. This approach was used to compare ECG wavelet entropy in subjects during resting and stressor conditions. The interactive visual representation allows a detailed examination of wavelet entropy, revealing time-varying differences in the complexity of the underlying ECGs that are not evident in traditional measures.