Electrocardiograms (ECGs) are essential tools for assessing cardiac health. The extraction of meaningful features from multivariate ECG data is critical for identifying potential cardiac anomalies based on inter-channel dynamics. This work introduces an innovative approach rooted in functional data analysis (FDA) aimed at analyzing the temporal and frequency-domain dynamics of multivariate ECG signals. Considering each patient’s heartbeats as a sample observation of a multivariate functional time series, this method employs the spectral density operator to reveal dynamic time-dependencies among ECG channels across frequency bands. Through the analysis of ECG data of a subject experiencing detachment from the self, this approach demonstrates its potential to uncover clinically relevant patterns that correlate with the patient’s symptomatology.