Candia-Rivera Diego, Catrambone Vincenzo, Valenza Gaetano
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:553-556. doi: 10.1109/EMBC44109.2020.9175226.
The growing interest in the study of functional brain-heart interplay (BHI) has motivated the development of novel methodological frameworks for its quantification. While a combination of electroencephalography (EEG) and heartbeat-derived series has been widely used, the role of EEG preprocessing on a BHI quantification is yet unknown. To this extent, here we investigate on four different EEG electrical referencing techniques associated with BHI quantifications over 4-minute resting-state in 15 healthy subjects. BHI methods include the synthetic data generation model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient (MIC). EEG signals were offline referenced under the Cz channel, common average, mastoids average, and Laplacian method, and statistical comparisons were performed to assess similarities between references and between BHI techniques. Results show a topographical agreement between BHI estimation methods depending on the specific EEG reference. Major differences between BHI methods occur with the Laplacian reference, while major differences between EEG references are with the MIC analysis. We conclude that the choice of EEG electrical reference may significantly affect a functional BHI quantification.
对功能性脑-心相互作用（BHI）研究兴趣的日益增长，推动了用于其量化的新型方法框架的发展。虽然脑电图（EEG）和心跳衍生序列的组合已被广泛使用，但EEG预处理在BHI量化中的作用尚不清楚。在此范围内，我们在15名健康受试者的4分钟静息状态下，研究了与BHI量化相关的四种不同的EEG电参考技术。BHI方法包括合成数据生成模型、心跳诱发电位、心跳诱发振荡和最大信息系数（MIC）。EEG信号在离线状态下采用Cz通道参考、公共平均参考、乳突平均参考和拉普拉斯方法进行参考，并进行统计比较以评估不同参考之间以及BHI技术之间的相似性。结果表明，根据特定的EEG参考，BHI估计方法之间存在地形一致性。BHI方法之间的主要差异出现在拉普拉斯参考中，而EEG参考之间的主要差异出现在MIC分析中。我们得出结论，EEG电参考的选择可能会显著影响功能性BHI量化。
