UNCORRECTED PROOF Epileptic seizures are characterized by changing signal complexity Gregory K. Bergey * , Piotr J. Franaszczuk Department of Neurology, Meyer 2-147, Johns Hopkins Epilepsy Center, Johns Hopkins University School of Medicine, 600 North Wolfe Street, Baltimore, MD 21287, USA Accepted 24 November 2000 Abstract Objective: Epileptic seizures are brief episodic events resulting from abnormal synchronous discharges from cerebral neuronal networks. The traditional methods of signal analysis are limited by the rapidly changing nature of the EEG signal during a seizure. Time±frequency analyses, however, such as those produced by the matching pursuit (MP) method can provide continuous decompositions of recorded seizure activity. These accurate decompositions can allow for more detailed analyses of the changes in complexity of the signal that may accompany seizure evolution. Methods: The MP algorithm was applied to provide time±frequency decompositions of entire seizures recorded from depth electrode contacts in patients with intractable complex partial seizures of mesial temporal onset. The results of these analyses were compared with signals generated from the Duf®ng equation that represented both limit cycle and chaotic behavior. Results: Seventeen seizures from 12 different patients were analyzed. These analyses reveal that early in the seizure, the most organized, rhythmic seizure activity is more complex than limit cycle behavior, and that signal complexity increases further later in the seizure. Conclusions: Increasing complexity routinely precedes seizure termination. This may re¯ect progressive desynchronization. q 2001 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Seizures; Epilepsy; Termination; Complexity; Signal analysis; Electroencephalography 1. Introduction All cerebral activity detectable by electroencephalogra- phy (EEG) is a re¯ection of synchronous neuronal activity, so synchronous neuronal activity per se is not abnormal. Epileptic seizures, however, are abnormal, temporary mani- festations of dramatically increased neuronal synchrony, either occurring regionally (partial seizures) or bilaterally (generalized seizures) in the brain. The mechanisms that may contribute to or cause this increased synchrony have been the subject of numerous studies focusing on the cellu- lar mechanisms of decreased inhibition and increased exci- tation. Recently, there has been interest in examining macroscopic EEG changes in neural and cerebral synchrony using various non-linear dynamic approaches (Blinowska and Malinowski, 1991; Casdagli et al., 1996; Elger and Lehnertz, 1998; Martinerie et al., 1998; Pijn et al., 1991, 1997; Pritchard and Duke, 1992; Pritchard et al., 1995; Schiff, 1998; Theiler and Rapp, 1996). There is presently no standard mathematical model of EEG activity. There- fore, investigators have been using various methods of signal analysis to describe the stochastic and deterministic features of these signals. The most orderly synchronous activity can be represented by strictly periodic signals of low complexity. Less synchronous activity, re¯ecting a less orderly state, can be represented by signals of increased complexity with multiple frequencies, quasiperiodic signals, and increasingly chaotic behavior of the signal. The period between seizures (interictal period) represents a relatively less orderly state of relatively low neuronal synchrony. Non-linear analyses have suggested that the seizure onset may represent a transition from this interictal period to one of increased synchronous activity, and that a more orderly state characterizes an epileptic seizure (Iase- midis and Sackellares, 1996). The changes occurring during a seizure, however, have not been as well studied because of the rapidly changing nature of the signal. One of the problems inherent in applying these methods of signal analysis to the recordings of actual seizures is that most linear and non-linear methods require long periods of relatively stationary activity. Epileptic seizures, however, are characteristically rapidly changing, dynamic phenom- ena. For the analysis of such signals with multiple frequen- Clinical Neurophysiology xx (2001) xxx±xxx 1388-2457/01/$ - see front matter q 2001 Elsevier Science Ireland Ltd. All rights reserved. PII: S1388-2457(00)00543-5 www.elsevier.com/locate/clinph CLINPH 2000038 * Corresponding author. Tel.: 11-410-955-7338; fax: 11-410-614-1569. E-mail address: gbergey@jhmi.edu (G.K. Bergey).