Atrial fibrillation (AFib) is the most common cardiac arrhythmia,
affecting eventually up to a quarter of the population. The purpose of
this small scale clinical study was to validate the usability of MEMS
accelerometer based bedsensor for detection of AFib. A Murata
accelerometer based ballistocardiogram bedsensor was attached under the
hospital bed magnetically and measurement data was recorded from 20 AFib
patients and 15 healthy volunteers, mainly females. The recording time
was up 30 minutes. The sensor built-in algorithms automatically
extracted features such as heart rate (HR), heart rate variability
(HRV), relative stroke volume (SVOL), signal strength (SS) and whether
the patient is in bed or not. We calculated median values for each
feature HR, HRV, SVOL and SS, and investigated whether it is possible to
separate AFib from healthy with these features or their combinations.
Areas under the curve (AUC) were 0.98 for full length signals and 0.85
for 3 min signal segments using random forest (RF) classifier
corresponding to sensitivity and specificity of 100% and 93.3% for full
length signals and 90% and 80% for 3 min signals. We conclude, that
based on our pilot results, the Murata bedsensor is able to detect AFib,
and seems to be a promising technology for long-term monitoring of AFib
at home settings as it requires only one-time installation and
operational time can be up to years and even tens of years.