Absence seizure prediction using recurrent neural networks

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Absence seizures have a real-life impact on epileptic subjects, as day-to-day tasks can by suddenly interrupted making for dangerous situations. Though a lot of work has been done on seizure detection, to limit the impact on epileptic patients, the true necessity lies in timely prediction of seizures before they manifest. Various attempts have been made using conventional algorithms to accurately predict seizures however, so far, results are not that encouraging. In this work, we applied various machine-learning algorithms, in an attempt to identify complex, multi-dimensional epileptic precursors in brain recordings. Three types of neural networks are used in this feasibility study, namely Multi-Layer Perceptron (MLP) networks, Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks. The used input data was annotated Electrocorticography (ECoG) data, recorded in living mutant rodents, containing epileptic events at an interval of about one minute. The data was pre-processed for better learning performance by data normalisation and by generating distinctive training features. The neural networks were configured as three-class classifiers, distinguishing among inter-ictal, pre-ictal and ictal periods. A grid-search approach was applied to determine the best set of parameters for the neural networks. Despite our best efforts, the relation between the input data and output data could not be learned in a reliable way. The maximum reached Average Prediction Rate (APR) was 0.57 with a prediction time of 3.1s when using the normalised data as input and 0.65 with a prediction time of 6.1s when using the distinctive features as input. These results essentially signify good detection but virtually no prediction of upcoming seizures. The evaluation of the experimental findings has revealed that the employed ECoG recordings were ill-selected for training our various neural-network models. Also, a non-conclusive exploratory experiment is performed by applying a Weibull Time-To-Event Recurrent Neural Network (WTTE-RNN) on a sub-set of the normalised input data. The experiment has yielded some positive results, a short-notice prediction of the upcoming seizure in some cases, encouraging for further exploration of this approach. Despite the limited success of this work, however, through its extended forensics analysis, it has paved the crucial, initial steps in the direction of seizure prediction.