Bed sensor ballistocardiogram for non-invasive detection of atrial fibrillation: a comprehensive clinical study.

Abstract

Atrial fibrillation (AFib) is a prevalent cardiac arrhythmia associated with significant morbidity and mortality. Early detection and continuous monitoring of AFib are crucial for prevention of complications such as stroke. In this paper, we explore the potential of using a ballistocardiogram (BCG) based bed-sensor to detect AFib through a comprehensive clinical study consisting of night hospital recordings for 116 patients split into 72 training subjects and 44 test subjects. The study uses established methods such as autocorrelation in order to detect AFib from the BCG signals. Spot and continuous Holter ECG were used as the reference methods to detect AFib against which the BCG rhythm classifications were compared Our findings suggest that this innovative approach holds promise for accurate and non-invasive continuous monitoring of AFib, contributing to improved patient care and outcomes. With full overnight recordings we were able to detect AFib with 94% accuracy with the train set by using a rule-based method and achieving AUROC score of 97% for the test set using a machine learning model trained with the training set.