Abstract
Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classification approach. Results and conclusions: The experimental analysis, performed by inspection and by the proposed threshold-based classifier on all the patients of the database, shows that the features extracted by the synchronization measures are able to detect preictal and ictal states and allow the prediction of the seizures few minutes before the seizure onsets.
Highlights
Epilepsy is a disease characterized by an enduring predisposition to generate epileptic seizures, due to abnormal excessive or synchronous neuronal activity in the brain [1]
We present a classification algorithm for seizure prediction that employs phase-synchronization measures computed for all pairs of EEG signals, namely, Phase Lag Index (PLI) and Weighted Phase Lag Index (WPLI) [18,19]
We perform an analysis by inspection, by detecting the channel pairs on which the features extracted by the synchronization measures show useful trends for seizure prediction
Summary
Epilepsy is a disease characterized by an enduring predisposition to generate epileptic seizures, due to abnormal excessive or synchronous neuronal activity in the brain [1]. The main feature of epilepsy, epileptic seizures, is associated with a number of negative consequences at both short- and long-term, including the risk of falls and injuries; eventual death; psychiatric disturbances; cognitive deficits; and difficulties in achieving academic, social, and employment goals. Antiepileptic drugs have limitations and side effects [2], and often fail to control seizures in ~30% of the cases, while surgery cannot always applied. Seizure prediction methods could be an important option. An early prediction might allow patients or caregivers to take suitable actions, such as warning an alarm, applying short-acting drugs, and activating stimulating devices
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