Multichannel ECG Spectral Analysis via Functional Data Methods: A Structured Approach to Dynamic Signal Dependencies

Abstract

Electrocardiogram (ECG) feature extraction is fundamental for detecting pathological conditions, yet traditional methods based on statistical summaries often fail to capture subtle morphological alterations. In this study, we adopt a functional spectral approach to analyze multichannel ECG data, preserving its temporal structure. We compare two methodologies for estimating the spectral density operator: Graphical Functional Principal Component Analysis (GFPCA), which computes cross-covariance at individual time points, and freqdom.fda, which extracts spectral features from basis expansion coefficients. In a case study of detachment episodes, freqdom.fda shows a better bias-variance tradeoff, effectively filtering noise while preserving key spectral patterns distinguishing regular and symptomatic states.