ClassA Entropy for the analysis of structural complexity of physiological signals

Despite the recent theoretical boom in Sample Entropy based algorithms for the analysis of physiological and pathological systems, the major issue which prevents their more widespread use remains that of large computational load, particularly in the studies of quantification of structural richness in data. This issue becomes even more prohibitive when it comes to large data sizes and real-time processing. To this end, a new Classification Angle (ClassA) Entropy is introduced for the quantification of structural complexity of real world signals, based on an improved Second Order Difference Plot and Shannon Entropy. In comparison with existing nonlinear techniques, including Real Sum Angle index, Phase Entropy, Gridded Distribution Entropy and Cosine Similarity Entropy, the proposed method offers the advantages of a minimal number of tunable parameters, lower requirement of data size, wider application field and large relaxation of computational load. Simulations on real world physiological data support the approach.