Epileptic Seizures are Characterized by Changing Signal Complexity
Objective: Epileptic seizures are brief episodic events
resulting from abnormal synchronous discharges from cerebral
neuronal networks. 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 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 matching pursuit 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 Duffing 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 reflect progressive
desynchronization.
Clinical Neurophysiology 112:241-249 (2001)
PDF file cn00.pdf