Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, MRC Oulu, University of Oulu, Oulu, Finland.
Department of Anesthesia, Intensive Care and Pain Medicine at South Carelia Central Hospital, Lappeenranta, Finland.
J Neural Eng. 2021 Oct 5;18(5). doi: 10.1088/1741-2552/ac23e2.
Electroencephalogram (EEG) recordings often contain large segments with missing signals due to poor electrode contact or other artifact contamination. Recovering missing values, contaminated segments and lost channels could be highly beneficial, especially for automatic classification algorithms, such as machine/deep learning models, whose performance relies heavily on high-quality data. The current study proposes a new method for recovering missing segments in EEG.In the proposed method, the reconstructed segment is estimated by substitution of the missing part of the signal with the normalized weighted sum of other channels. The weighting process is based on inter-channel correlation of the non-missing preceding and proceeding temporal windows. The algorithm was designed to be computationally efficient. Experimental data from patients (= 20) undergoing general anesthesia due to elective surgery were used for the validation of the algorithm. The data were recorded using a portable EEG device with ten channels and a self-adhesive frontal electrode during induction of anesthesia with propofol from waking state until burst suppression level, containing lots of variation in both amplitude and frequency properties. The proposed imputation technique was compared with another simple-structure technique. Distance correlation (DC) was used as a measure of comparison evaluation.: The proposed method, with an average DC of 82.48 ± 10.01 (± σ)%, outperformed its competitor with an average DC of 67.89 ± 14.12 (± σ)%. This algorithm also showed a better performance when increasing the number of missing channels.the proposed technique provides an easy-to-implement and computationally efficient approach for the reliable reconstruction of missing or contaminated EEG segments.
脑电图 (EEG) 记录常常包含由于电极接触不良或其他伪迹污染而导致的大量缺失信号的片段。恢复缺失值、污染段和丢失的通道可能非常有益，特别是对于自动分类算法，如机器/深度学习模型，其性能严重依赖于高质量的数据。本研究提出了一种新的方法来恢复 EEG 中的缺失段。在提出的方法中，通过用其他通道的归一化加权和替代信号的缺失部分来估计重建的段。加权过程基于非缺失的前后时间窗口的通道间相关性。该算法旨在提高计算效率。实验数据来自接受全身麻醉的患者（=20），这些患者由于择期手术而接受全身麻醉，数据是在使用便携式 EEG 设备和十个通道以及自粘额部电极从清醒状态诱导麻醉期间记录的，在此期间，振幅和频率特性都有很大的变化。所提出的插补技术与另一种简单结构技术进行了比较。距离相关 (DC) 被用作比较评估的度量。所提出的方法的平均 DC 为 82.48±10.01（±σ）%，优于其竞争对手的平均 DC 为 67.89±14.12（±σ）%。当增加缺失通道的数量时，该算法的性能也有所提高。所提出的技术为可靠地重建缺失或污染的 EEG 段提供了一种易于实现和计算高效的方法。
