Multilevel functional principal component analysis

TL;DR

This paper introduces a novel multilevel functional principal component analysis method to analyze complex hierarchical EEG data from sleep studies, revealing associations with cardiovascular health.

Contribution

The paper presents a new statistical methodology, MFPCA, for analyzing multilevel functional data, applicable to hierarchical and longitudinal studies.

Findings

Identified core EEG components linked to sleep stages.

Quantified associations between EEG activity and cardiovascular outcomes.

Demonstrated the method's applicability to large hierarchical datasets.

Abstract

The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes.