A new NLEO based technique for the detection of burst–suppression patterns in multichannel neonatal EEG signals

In this paper, we propose a new method, based on the nonlinear energy operator (NLEO), to automatically detect burst–suppression (B–S) patterns in multichannel newborn electroencephalograms (EEGs). The proposed approach consists of two algorithms: (1) per-brain region B–S detection and (2) global B–S detection. At first, B–S patterns are detected in each channel using NLEO. Average of NLEO values obtained for all the channels is then calculated to detect the presence of B–S patterns in each brain region. After local B–S detection, the global B–S detection algorithm classifies a sample-point as burst if most of regions are bursting. Otherwise, the sample-point is classified as suppression. The proposed method is validated using a database composed of multichannel EEG signals acquired from 6 neonates. The experimental results show that the proposed approach can detect bursts which occur locally and classify global B–S patterns with a very high accuracy of 98%.