Global ordinal pattern attention entropy: A novel feature extraction method for complex signals

Entropy serves as an effective method for quantifying the irregularity and complexity of nonlinear time series or complex signals. Recently, a novel entropy measure, attention entropy (AE), has been introduced for detecting interbeat interval time series. However, the original AE focuses solely on peak points, potentially overlooking crucial information embedded in signals. In this paper, we present the global ordinal pattern attention entropy (GOPAE), a novel measure that integrates AE with the principles of phase space reconstruction (PSR). Additionally, the connections between GOPAE and state-of-the-art time series network methods, including ordinal pattern transition network (OPTN) and recurrence quantification analysis (RQA), are elucidated to showcase its proficiency in extracting dynamic information from complex signals. Comparative experiments, both qualitative and quantitative, are conducted, using both simulated data and real-world signals. The results of the experiments suggest that GOPAE can effectively distinguishing complex signals in real application scenarios.