  The high rate of false alarms in intensive care units (ICUs) is one of the
top challenges of using medical technology in hospitals. These false alarms are
often caused by patients' movements, detachment of monitoring sensors, or
different sources of noise and interference that impact the collected signals
from different monitoring devices. In this paper, we propose a novel set of
high-level features based on unsupervised feature learning technique in order
to effectively capture the characteristics of different arrhythmia in
electrocardiogram (ECG) signal and differentiate them from irregularity in
signals due to different sources of signal disturbances. This unsupervised
feature learning technique, first extracts a set of low-level features from all
existing heart cycles of a patient, and then clusters these segments for each
individual patient to provide a set of prominent high-level features. The
objective of the clustering phase is to enable the classification method to
differentiate between the high-level features extracted from normal and
abnormal cycles (i.e., either due to arrhythmia or different sources of
distortions in signal) in order to put more attention to the features extracted
from abnormal portion of the signal that contribute to the alarm. The
performance of this method is evaluated using the 2015 PhysioNet/Computing in
Cardiology Challenge dataset for reducing false arrhythmia alarms in the ICUs.
As confirmed by the experimental results, the proposed method offers a
considerable performance in terms of accuracy, sensitivity and specificity of
alarm detection only using a few high-level features that are extracted from
one single lead ECG signal.
