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

作者：Mirzaei Parisa; Azemi Ghasem^*

来源：Analog Integrated Circuits and Signal Processing, 2017, 92(2): 255-262.

DOI：10.1007/s10470-017-0989-0

• Burst-suppression
• Newborn
• NLEO
• Automated detection
• EEG

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%.

• 出版日期2017-8