Low-Rank Tensor Completion for Heart Failure Exacerbation Detection in Multivariate Time Series with Missing Data

Heart failure exacerbations (HFE) represent a critical challenge in healthcare due to their significant role in global mortality. The rise of home and wearable devices capable of monitoring cardiac conditions provides valuable opportunities for data-driven analysis and HFE detection. However, these devices frequently generate low-quality measurements with irregular sampling frequencies and high rates of missing data. Our paper presents a methodology that processes these measurements as a three-dimensional tensor, applying a low-rank tensor completion scheme to manage missing data effectively, thus facilitating anomaly detection without necessitating data imputation. We validate our method on a dataset from 4 patients with chronic HF in the compensation phase, collected at the Hospital Fondazione Policlinico Universitario Campus Bio-Medico in Rome, Italy. Our results demonstrate the tensor-based method’s superiority over traditional techniques, highlighting its potential for detecting anomalies within complex multivariate time series data. This research emphasizes the critical role of advanced data analysis in enhancing HFE identification, which could lead to improved patient care and reduced hospitalization rates.