A Feasibility Study on the Automatic Detection of Atrial Fibrillation using an Unobtrusive Bed-Mounted Sensor

We present a feasibility study on the automatic detection of atrial fibrillation (AF) from a cardiac vibration signal (ballistocardiogram). Signals were recorded by means of an electromechanical foil attached to a bed’s mattress. A clinical study with 10 AF patients was conducted to assess whether ballistocardiograms (BCG) provide sufficient in-formation to automatically distinguish atrial fibrillations from normal sinus rhythms. For this purpose, the BCGs were split into 30 s long epochs which were manually la-belled as AF or sinus rhythm. Using features extracted from an auto-regressive time-frequency representation of the BCG, a support vector machine classifier was trained to detect AF epochs. The classifier was evaluated on a set of 245 epochs by means of leave-one-out cross-validation. Our results (sensitivity: 96.17 % / specificity: 91.94 %) suggest that it is indeed feasible to use bed-mounted BCG sensors to screen for atrial fibrillations. 1