title: Suppression of false arrhythmia alarms in the ICU: a machine learning approach.
abstract: This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of beat detection algorithms, some of which are developed by the authors. The outputs of the beat detection algorithms are combined using a machine learning approach. For the ventricular tachycardia and ventricular fibrillation alarms, separate classification models are trained to distinguish between the normal and abnormal beats. This information, along with alarm-specific criteria, is used to decide if the alarm is false. The results indicate that the presented method was effective in suppressing false alarms when it was tested on a hidden validation dataset.
authors.affiliation: Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA. Michigan Center for Integrative Research in Clinical Care, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Sardar
authors.initials: S
authors.lastname: Ansari
authors.forename: Ashwin
authors.initials: A
authors.lastname: Belle
authors.forename: Hamid
authors.initials: H
authors.lastname: Ghanbari
authors.forename: Mark
authors.initials: M
authors.lastname: Salamango
authors.forename: Kayvan
authors.initials: K
authors.lastname: Najarian
mesh_terms: D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D056902:Clinical Alarms; D004562:Electrocardiography / Q000295:instrumentation; D005189:False Positive Reactions; D006339:Heart Rate; D006801:Humans; D007362:Intensive Care Units; D000069550:Machine Learning; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted