title: Signal quality and data fusion for false alarm reduction in the intensive care unit.
abstract: 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.affiliation: Institute of Biomedical Engineering, School of Medicine, Shandong University, Jinan, Shandong, China.
authors.forename: Qiao
authors.initials: Q
authors.lastname: Li
authors.forename: Gari D
authors.initials: GD
authors.lastname: Clifford
mesh_terms: D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D001185:Artificial Intelligence; D056902:Clinical Alarms; D003422:Critical Care / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003951:Diagnostic Errors / Q000517:prevention & control*; D005189:False Positive Reactions; D006801:Humans; D008991:Monitoring, Physiologic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity