Objective. This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy Assist Device) in a pseudo-prospective mode using iEEG from four canines with naturally occurring epilepsy. Approach. The system was trained and tested on 75 seizures collected over 1608 d utilizing a genetic algorithm to optimize forecasting hyper-parameters (prediction horizon (PH), median filter window length, and probability threshold) for each subject-specific seizure forecasting model. The trained CNN models were deployed on a hand-held tablet computer and tested on testing iEEG datasets from four canines. The results from the iEEG testing datasets were compared with Monte Carlo simulations using a Poisson random predictor with equal time in warning to evaluate seizure forecasting performance. Main results. The results show the CNN models forecasted seizures at rates significantly above chance in all four dogs (p < 0.01, with mean 0.79 sensitivity and 18% time in warning). The deep learning method presented here surpassed the performance of previously reported methods using computationally expensive features with standard machine learning methods like logistic regression and support vector machine classifiers. Significance. Our findings principally support the feasibility of deploying trained CNN models on a hand-held computational device (Mayo Epilepsy Assist Device) that analyzes streaming iEEG data for real-time seizure forecasting.

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The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.