A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings
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A SIGNAL REGULARITY-BASED AUTOMATED SEIZURE PREDICTION ALGORITHM USING LONG-TERM SCALP EEG RECORDINGS1 Jui-Hong Chien,a Deng-Shan Shiau,b J. J. Halford,c K. M. Kelly,d R. T. Kern,e M. C. K. Yang,f Jicong Zhang,g J. Ch. Sackellares,h and P. M. Pardalosi
UDC 519.6
Abstract. The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS (“PMRS entrainment”) among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure. Keywords: epileptic seizure, seizure warning, scalp electroencephalogram, brain dynamics. 1. INTRODUCTION An epileptic seizure is a transient occurrence of signs and/or symptoms due to abnormal excessive and synchronous neuronal activity in the brain [1]. The worldwide prevalence of epilepsy ranges from 0.4% to 1% [2]. Some epileptic patients experience a prodrome or an aura [3], which can serve as a warning before frank signs of seizure onset. Rare patients learn to abort a seizure without external intervention [4]. However, most patients cannot predict or arrest their seizures. In industrialized countries, where antiepileptic drugs and seizure control devices are readily available, about 70% of epilepsy patients are able to gain satisfactory control of their seizures [5]. For patients whose seizures do not respond to antiepileptic medications, less than 50% are candidates for epilepsy surgery [6]. Therefore, approximately 15–20% of epilepsy patients have no choice but to live their lives with unforeseen and uncontrolled seizure attacks, which cause considerable stress for these patients and their care-givers and limit the range of daily activities available due to safety concerns. These lifestyle limitations decrease quality of life and may contribute to the increased prevalence of depression in patients with uncontrolled seizures [7]. If a device could be developed that could warn an epilepsy patient of an impending seizure, it could lessen the psychological stress of epilepsy and improve patient safety. Results from several studies based on the analysis of intracranial EEG [8] and fMRI [9] data suggest the existence of a preictal transition between an interictal and ictal state. However, detecting a preictal transition using EEG signals from 1
This work was supported by the grants 5R01NS050582 (JCS) and 1R43NS064647 (DSS) from NIH-NINDS.
a Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA, and Optima Neuroscience, Inc., Gainesville, USA, [email protected]. bScientific Affairs, Gainesville, FL, USA, and Optima Neuroscience, Inc., Gainesville, USA, [email protected]. cMedical University of South Carol
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