Low Precision Electroencephalogram for Seizure Detection with Convolutional Neural Network

Abstract

Electroencephalogram (EEG) neural activity recording has been widely used for diagnosing and monitoring epileptic patients. Ambulatory epileptic monitoring devices that can detect or even predict seizures play an important role for patients with intractable epilepsy. Though many EEG-based seizure detection algorithms have been proposed in the literature with high accuracy, their hardware implementations are constrained because of power consumption. Many commercial non-research EEG monitoring systems samples multiple electrodes at a relatively high rate and transmit the data either via a wire or wirelessly to an external signal processing unit. In this work, we studied how a reduced sampling precision affects the performance of our machine learning signal processing in seizure detection. To answer this question, we reduce the number of bits (precision) in an analog-to-digital converter (ADC) used in an EEG recorder. The outcome shows that the reduction of ADC precision down to 6-bit does not significantly reduce our convolutional neural network performance in detecting seizure onsets. As an indication of the performance, we achieved an area under the curve (AUC) more than 92% and above 96% on Freiburg Hospital and the Boston Children’s Hospital-MIT seizure datasets, respectively. A possible reduction in ADC precision not only contribute to energy consumption reduction, particularly if the data has to be transmitted, but also offers an improved computational efficacy regarding memory requirement and circuit area.