Relationship between approximate entropy and visual inspection of irregularity in the EEG signal, a comparison with spectral entropy  A. Anier, T. Lipping, R. Ferenets, P. Puumala, E. Sonkajärvi, I. Rätsep, V. Jäntti  British Journal of Anaesthesia  Volume 109, Issue 6, Pages 928-934 (December 2012) DOI: 10.1093/bja/aes312 Copyright © 2012 The Author(s) Terms and Conditions

Fig 1 Waveforms (left column) and corresponding amplitude spectra (right column) of signals characterized by fixed spectral entropy (SpEn) of 0.24. In time domain, a 2 s excerpt of the waveforms is shown. The phase spectrum of all the surrogates is randomized. The approximate entropy (ApEn) estimate [mean (sd)] over 20 realizations of the surrogates is indicated above each row of the figure. The characteristic frequency of the original sawtooth wave is 10.1 Hz and the sampling frequency is 500 Hz. British Journal of Anaesthesia 2012 109, 928-934DOI: (10.1093/bja/aes312) Copyright © 2012 The Author(s) Terms and Conditions

Fig 2 Sensitivity of spectral entropy and approximate entropy to rhythmic activity of the α-frequency band in the EEG. The proportion of the rhythmic activity in the signal is modified in eight steps using the ARMA modelling technique and the corresponding power spectra are plotted (upper left plot). From 50 realizations of the modified signals for each eight levels of the rhythmic activity, the spectral entropy and approximate entropy measures were calculated and presented as box plots (upper middle and right). Statistical significance of the trends of the entropy measures are indicated below the box plots: the bottommost mark indicates the result of comparing the corresponding peak magnitude to the case of maximally flat power spectrum (the leftmost box). A star indicates statistically significant difference. Samples of time domain waveforms of the modified signals are shown in the bottom half of the figure. British Journal of Anaesthesia 2012 109, 928-934DOI: (10.1093/bja/aes312) Copyright © 2012 The Author(s) Terms and Conditions

Fig 3 The effect of the relative increase in rhythmic activity on power spectra and the entropy measures. From 1 to 3, the amount of slow activity decreases and rhythmic activity increases. The logarithmic power spectra are scaled according to the maximum of the δ-frequency peak. This illustrates the flattening of the spectrum which causes the increase in spectral entropy. At the same time, the increase in the relative amplitude of rhythmic activity is reflected by approximate entropy as increasing regularity and hence decreasing entropy value. The two entropy measures change in different directions from 1 to 3. British Journal of Anaesthesia 2012 109, 928-934DOI: (10.1093/bja/aes312) Copyright © 2012 The Author(s) Terms and Conditions