Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.
BIND - Behavioral Imaging and Neural Dynamics Center, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy.
PeerJ. 2022 Jul 12;10:e13734. doi: 10.7717/peerj.13734. eCollection 2022.
Artefact removal in neonatal electroencephalography (EEG) by visual inspection generally depends on the expertise of the operator, is time consuming and is not a consistent pre-processing step to the pipeline for the automated EEG analysis. Therefore, there is the need for the automated detection and removal of artefacts in neonatal EEG, especially of distinct and predominant artefacts such as flat line segments (mainly caused by instrumental error where contact between electrodes and head box is lost) and large amplitude fluctuations (related to neonatal movements).
A threshold-based algorithm for the automated detection and removal of flat line segments and large amplitude fluctuations in neonatal EEG of infants at term-equivalent age is developed. The algorithm applies thresholds to the absolute second difference, absolute amplitude, absolute first difference and the ratio between the frequency content above 50 Hz and the frequency content across all frequencies.
The algorithm reaches a median accuracy of 0.91, a median hit rate of 0.91 and a median false discovery rate of 0.37. Also, a significant improvement (≈10%) in the performance of a four-stage sleep classifier is observed after artefact removal with the proposed algorithm as compared to before its application.
An automated artefact removal method contributes to the pipeline of automated EEG analysis. The proposed algorithm has shown to have good performance and to be effective in neonatal EEG applications.
通过视觉检查去除新生儿脑电图（EEG）中的伪影通常依赖于操作人员的专业知识，既耗时又不能作为自动化 EEG 分析流水线的一致预处理步骤。因此，需要在新生儿 EEG 中自动检测和去除伪影，特别是明显和主要的伪影，如平线段（主要由电极与头盒之间的接触丢失引起的仪器误差引起）和大振幅波动（与新生儿运动有关）。
开发了一种基于阈值的算法，用于自动检测和去除足月新生儿 EEG 中的平线段和大振幅波动。该算法将阈值应用于绝对二阶差分、绝对幅度、绝对一阶差分以及 50 Hz 以上频率分量与所有频率分量之间的频率分量之比。
该算法的中位数准确率为 0.91，中位数命中率为 0.91，中位数假阳性率为 0.37。此外，与应用前相比，使用所提出的算法去除伪影后，四阶段睡眠分类器的性能显著提高（≈10%）。
自动去除伪影的方法有助于自动化 EEG 分析的流水线。所提出的算法在新生儿 EEG 应用中表现出良好的性能和有效性。
