Dual Adaptive Grouped Multivariate Variational Mode Decomposition for Physiological Signals.

Abstract

Objective: Grouped multivariate variational mode decomposition (GMVMD) is sensitive to the preset group number $G$ and common mode number $K$. To improve its adaptability to multichannel electroencephalogram (EEG) and electrocardiogram (ECG) signals, we determine both automatically.

Methods: We propose dual adaptive grouped multivariate variational mode decomposition (DA-GMVMD) with three stages: adaptive grouping, adaptive $K$ search and grouped decomposition. Adaptive grouping constructs a channel graph and determines the channel groups using the Laplacian eigengap and spectral clustering. Adaptive $K$ search uses probe-mode energy shares to select a mode number for each group. Grouped decomposition applies multivariate variational mode decomposition (MVMD) to each group and combines the results.

Results: On synthetic signals, DA-GMVMD recovers the underlying grouping structure and remains stable across noise levels. In a separate synthetic test, it recovers the group-specific mode numbers. On the CHB-MIT, MODMA, and PTB-XL datasets, it achieves lower reconstruction error than GMVMD with fixed parameters and several baselines related to MVMD. The reduction relative to GMVMD is significant on CHB-MIT and MODMA. On PTB-XL, DA-GMVMD has a lower observed mean reconstruction error. The reconstructed signals retain seizure classification information on CHB-MIT, regional band power and bilateral asymmetry on MODMA, and R-peak timing and ECG intervals on PTB-XL.

Conclusion: DA-GMVMD adapts the channel groups and their mode numbers to the signal content while retaining the evaluated EEG and ECG information in the reconstructed signals.

Significance: DA-GMVMD makes multichannel EEG and ECG decomposition less sensitive to a manually preset group number and common mode number.