https://www.selleckchem.com/JAK.html With the same dataset, the performances of the proposed method were generally higher than those of conventional methods for AD classification based on Pearson's correlation network, dynamic Pearson's correlation network, High-order correlation network, and causality correlation network. Our method demonstrates the measure of Sample Entropy with causality connection as a powerful tool to classify AD patients from CN controls, and provides a deep insight into the neuropathogenesis of AD. Our method demonstrates the measure of Sample Entropy with causality connection as a powerful tool to classify AD patients from CN controls, and provides a deep insight into the neuropathogenesis of AD. The choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet. This study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient. The SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement. The use of EEG reference based on a common average outperforms on the use of other references fo