Measures of Complexity Information theory estimates Algorithmic complexity

Measures of Complexity

• Information theory estimates • Algorithmic complexity • Hidden Markov

• Approximate entropy – Probability that 2 sequences which are similar for m points, remain similar at the next point

• Anaesthesia depth discrimination from EEG data, deeper • • anaesthesia means lower complexity Characterization of mentally pathological and healthy groups from EEG recordings Discrimination of different stages of sleep from EEG and respiratory motion, lower complexity during deep sleep

• Sample entropy – Modification of approximate entropy and self-matches exclusion

• Neonatal sepsis prediction from HRV data

• Fourier entropy – – – Compute PSD of a time series Normalize spectrum to get probability-like distribution Calculate entropy of normalized spectrum

• Wavelet entropy – Compute wavelet spectrum of a time series – Calculate wavelet entropy

• Wavelet entropy of pigs ECG

• Renyi entropy – – – Compute time-frequency representation of a time series Count connected regions above some threshold One peak in time-frequency space represents an elementary event – Counting peaks gives an estimate of complexity

• Higher order methods – Break a time series into parts – Compute entropy of each part – Treat entropy values as a time series and compute entropy of that sequence

• Multiscale methods – Downsampling of a time series – Calculate entropy of downsampled data

• MSE entropy of HRV signal