Evaluation of Capacitive ECG for Unobtrusive Atrial Fibrillation Monitoring

Abstract

Unobtrusive collection of vital signs using sensors embedded in beds, chairs, and automobile seats can longitudinally monitor patients for abnormal heart conditions outside of the hospital to inform both preventative and postdiagnosis care. The capacitive electrocardiogram (cECG) shows potential for collecting electrical information about a patient's heart without requiring skin contact like regular electrocardiogram (ECG). However, motion artifacts and environmental factors easily corrupt cECG signal quality and reduce the diagnostic value of unobtrusively collected cECG. To evaluate the atrial fibrillation (AF) screening performance of cECG compared to ECG, we conduct three different experiments on the clinical UnoViS dataset consisting of 55 min of concurrent ECG and cECG signals from 92 patients with manual clinician annotations of AF. First, we trained and evaluated models to detect AF events on cECG and ECG separately. Then, we trained a model using ECG and evaluated it on cECG to measure the interchangeability of the ECG and cECG domains. For each experiment five-fold subjectwise, class-stratified cross-validation was used to assess trained algorithm performance on hold-out test sets and three different algorithmic methodologies were assessed. Although evaluating the trained ECG model on cECG data (AUC: 0.874 ± 0.067, F1-score: 0.553 ± 0.148) performed slightly worse than evaluating on ECG, it did perform better than the model trained and evaluated on cECG alone, which suggests that utilizing ECG data for model training can effectively screen similar conditions in cECG data.