Multichannel EEG based inter-ictal seizures detection using Teager energy with backpropagation neural network classifier.

Abstract

A long-term multichannel electroencephalogram recording plays a crucial role in recognizing the epileptic seizure activities from the brain lobes. This research study investigates the automated detection of epileptic seizures from multichannel electroencephalogram recordings using Teager energy feature. A supervised back-propagation neural network model was implemented to classify the inter-ictal seizures. The study was conducted on multichannel electroencephalogram data that was obtained from Institute of Neuroscience, Ramaiah Memorial Hospital, Bengaluru, India, after ethical clearance from the from the Institutional Ethics Board. Initially, notch filter was applied to remove the 50Hz power line noise from raw electroencephalogram followed by independent component analysis to remove eye blinks and muscular activities. A time domain feature called Teager energy was estimated which detects the rapid changes in the given electroencephalogram time series. A 1s windowing was introduced to ensure stationarity for estimation of Teager energy. The descriptive and box plot analysis ensures the suitability of the Teager energy for the seizure detection. The performance of the multilayer perceptron neural network classifier was evaluated using sensitivity, specificity, and false detection rate. Simulation results showed the highest sensitivity, specificity and false detection rate of 96.66%, 99.15%, and 0.30 per hour respectively. It can be concluded that procedure can be applied for real-time seizure detection.