A Multilayer Visibility Graph Framework for Ultrashort-Term Analysis of Multichannel Physiological Signals

Recent advances in physiological sensing technologies enable continuous acquisition of multichannel biosignals for digital health applications. Because physiological interactions arise from nonlinear mechanisms, their characterization requires analytical approaches capable of capturing both individual signal dynamics and cross-signal dependencies, often from short recordings. This study proposes a multilayer visibility graph (VG) framework free of phase-space embedding parameters for analyzing physiological interactions from multichannel biosignals. The approach was evaluated in a simulation study based on unidirectionally coupled Rössler oscillators and in two physiological datasets comprising synchronized cardiovascular recordings from healthy adults and wearable electrodermal activity (EDA) recordings acquired during a Mindfulness-Based Stress Reduction (MBSR) intervention. Average Degree (DA) was used to characterize the topological organization of individual and multiplex visibility graphs, while inter-layer mutual information (I) between degree sequences was introduced to quantify cross-layer dependence. In the simulation study, coupling-related changes in the response dynamics were already detectable from single-layer VGs at short window lengths, whereas inter-layer mutual information captured coupling-dependent changes in cross-layer organization. Feature estimates began to stabilize at approximately 480 samples and approached a plateau at longer window lengths. In the cardiovascular dataset, sex-related differences were primarily observed in configurations involving systolic blood pressure: DA differed in single-layer and multichannel representations, while inter-layer mutual information showed significant sex differences, specifically in the interbeat interval-systolic blood pressure duplex. In the EDA dataset, the proposed descriptors identified changes in trainer-trainee physiological organization across the MBSR intervention. These findings indicate that VG-based descriptors provide complementary information on within-signal topology and inter-layer dependence and can be applied to short multichannel physiological recordings without phase-space reconstruction.