Objective. Atrial fibrillation (AFib) is a common cardiac arrhythmia associated with high morbidity and mortality, making early detection and continuous monitoring essential to prevent complications like stroke. This study explores the potential of using a ballistocardiogram (BCG) based bed sensor for the detection of AFib.

Approach. We conducted a comprehensive clinical study with night hospital recordings from 116 patients, divided into 72 training and 44 test subjects. The study employs established methods such as autocorrelation to identify AFib from BCG signals. Spot and continuous Holter ECG were used as reference methods for AFib detection against which BCG rhythm classifications were compared.

Results. Our findings demonstrate the potential of BCG-based AFib detection, achieving 94% accuracy on the training set using a rule-based method. Furthermore, the machine learning model trained with the training set achieved an AUROC score of 97% on the test set.

Significance. This innovative approach shows promise for accurate, non-invasive, and continuous monitoring of AFib, contributing to improved patient care and outcomes, particularly in the context of home-based or hospital settings.