Document Type: Original Paper
M.Sc. in Control Engineering, Electrical Engineering Dept., Faculty of Engineering, Ferdowsi University, Mashhad, Iran
Assistant Professor, Electrical Engineering Dept., Faculty of Engineering, Ferdowsi University, Mashhad, Iran
Associate Professor, Electrical Engineering Dept., Faculty of Engineering, Ferdowsi University, Mashhad, Iran
Associate Professor, Community Medicine and Public Health Dept., Mashhad University of Medical Science, Mashhad, Iran
Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures.
Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures.
Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals.
Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.