Author:

Keywords:

Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Epilepsy, electroencephalography, absence seizures, signal processing, automated seizure detection, wearable EEG, embedded systems, EEG, EPILEPSY, NETWORK, Algorithms, Electroencephalography, Epilepsy, Absence, Humans, Seizures, Sensitivity and Specificity, Wearable Electronic Devices, STADIUS-20-41, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences, Artificial Intelligence & Image Processing, 46 Information and computing sciences

Abstract:

Advances in electroencephalography (EEG) equipment now allow monitoring of people with epilepsy in their daily-life environment. The large volumes of data that can be collected from long-term out-of-clinic monitoring require novel algorithms to process the recordings on board of the device to identify and log or transmit only relevant data epochs. Existing seizure-detection algorithms are generally designed for post-processing purposes, so that memory and computing power are rarely considered as constraints. We propose a novel multi-channel EEG signal processing method for automated absence seizure detection which is specifically designed to run on a microcontroller with minimal memory and processing power. It is based on a linear multi-channel filter that is precomputed offline in a data-driven fashion based on the spatial-temporal signature of the seizure and peak interference statistics. At run-time, the algorithm requires only standard linear filtering operations, which are cheap and efficient to compute, in particular on microcontrollers with a multiply-accumulate unit (MAC). For validation, a dataset of eight patients with juvenile absence epilepsy was collected. Patients were equipped with a 20-channel mobile EEG unit and discharged for a day-long recording. The algorithm achieves a median of 0.5 false detections per day at 95% sensitivity. We compare our algorithm with state-of-the-art absence seizure detection algorithms and conclude it performs on par with these at a much lower computational cost.