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The high rate of false alarms in intensive care units (ICUs) is one of thetop challenges of using medical technology in hospitals. These false alarms areoften caused by patients' movements, detachment of monitoring sensors, ordifferent sources of noise and interference that impact the collected signalsfrom different monitoring devices. In this paper, we propose a novel set ofhigh-level features based on unsupervised feature learning technique in orderto effectively capture the characteristics of different arrhythmia inelectrocardiogram (ECG) signal and differentiate them from irregularity insignals due to different sources of signal disturbances. This unsupervisedfeature learning technique, first extracts a set of low-level features from allexisting heart cycles of a patient, and then clusters these segments for eachindividual patient to provide a set of prominent high-level features. Theobjective of the clustering phase is to enable the classification method todifferentiate between the high-level features extracted from normal andabnormal cycles (i.e., either due to arrhythmia or different sources ofdistortions in signal) in order to put more attention to the features extractedfrom abnormal portion of the signal that contribute to the alarm. Theperformance of this method is evaluated using the 2015 PhysioNet/Computing inCardiology Challenge dataset for reducing false arrhythmia alarms in the ICUs.As confirmed by the experimental results, the proposed method offers aconsiderable performance in terms of accuracy, sensitivity and specificity ofalarm detection only using a few high-level features that are extracted fromone single lead ECG signal.

An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs