Signal quality and data fusion for false alarm reduction in the intensive care unit.

Journal article

Due to a lack of integration between different sensors, false alarms (FA) in the intensive care unit (ICU) are frequent and can lead to reduced standard of care. We present a novel framework for FA reduction using a machine learning approach to combine up to 114 signal quality and physiological features extracted from the electrocardiogram, photoplethysmograph, and optionally the arterial blood pressure waveform. A machine learning algorithm was trained and evaluated on a database of 4107 expert-labeled life-threatening arrhythmias, from 182 separate ICU visits. On the independent test data, FA suppression results with no true alarm (TA) suppression were 86.4% for asystole, 100% for extreme bradycardia and 27.8% for extreme tachycardia. For the ventricular tachycardia alarms, the best FA suppression performance was 30.5% with a TA suppression rate below 1%. To reduce the TA suppression rate to zero, a reduction in FA suppression performance to 19.7% was required.

Authors: Li, Q; Clifford, G

• Publication status: Published
• Journal: Journal of electrocardiology
• Volume: 45
• Issue: 6
• Pages: 596-603
• Publication date: 2012-01-01
• DOI: 10.1016/j.jelectrocard.2012.07.015
• EISSN: 1532-8430
• ISSN: 0022-0736
• Language: English
• Keywords: False Positive Reactions; Artificial Intelligence; Pattern Recognition, Automated; Diagnosis, Computer-Assisted; Reproducibility of Results; Monitoring, Physiologic; Humans; Clinical Alarms; Sensitivity and Specificity; Arrhythmias, Cardiac; Diagnostic Errors; Intensive Care