Highly efficient and accurate seizure prediction on constrained IoT devices

In this paper we present an efficient and accurate algorithm for epileptic seizure prediction on low-power and portable IoT devices. State-of-the-art algorithms suffer from two issues: computation intensive features and large internal memory requirement, which make them inapplicable for constrained devices. We reduce the memory requirement of our algorithm by reducing the size of data segments (i.e. the window of input stream data on which the processing is performed), and the number of required EEG channels. To respect the limitations of the processing capability, we reduce the complexity of our exploited features by only considering the simple features, which also contributes to reducing the memory requirements. Then, we provide new relevant features to compensate the information loss due to the simplifications (i.e. less number of channels, simpler features, shorter segment, etc.). We measured the energy consumption (12.41 mJ) and execution time (565 ms) for processing each segment (i.e. 5.12 seconds of EEG data) on a low-power MSP432 device. Even though the state-of-art does not fit to IoT devices, we evaluate the classification performance and show that our algorithm achieves the highest AUC score (0.79) for the held-out data and outperforms the state-of-the-art.