title: False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal.
abstract: Over the past few years, reducing the number of false positive cardiac monitor alarms (FA) in the intensive care unit (ICU) has become an issue of the utmost importance. In our work, we developed a robust methodology that, without the need for additional non-ECG waveforms, suppresses false positive ventricular tachycardia (VT) alarms without resulting in false negative alarms. Our approach is based on features extracted from the ECG signal 20 seconds prior to a triggered alarm. We applied a multi resolution wavelet transform to the ECG data 20seconds prior to the alarm trigger, extracted features from appropriately chosen scales and combined them across all available leads. These representations are presented to a L1-regularized logistic regression classifier. Results are shown in two datasets of physiological waveforms with manually assessed cardiac monitor alarms: the MIMIC II dataset, where we achieved a false alarm (FA) suppression of 21% with zero true alarm (TA) suppression; and a dataset compiled by UCSF and General Electric, where a 36% FA suppression was achieved with a zero TA suppression. The methodology described in this work could be implemented to reduce the number of false monitor alarms in other arrhythmias.
authors.affiliation: Department of Physiological Nursing, University of California, San Francisco, CA, USA. Electronic address: rebeca.salas-boni@ucsf.edu.
authors.forename: Rebeca
authors.initials: R
authors.lastname: Salas-Boni
authors.affiliation: Department of Bioengineering, University of California, Los Angeles, CA, USA.
authors.forename: Yong
authors.initials: Y
authors.lastname: Bai
authors.affiliation: Department of Physiological Nursing, University of California, San Francisco, CA, USA.
authors.forename: Patricia Rae Eileen
authors.initials: PR
authors.lastname: Harris
authors.affiliation: Department of Physiological Nursing, University of California, San Francisco, CA, USA.
authors.forename: Barbara J
authors.initials: BJ
authors.lastname: Drew
authors.affiliation: Department of Physiological Nursing, University of California, San Francisco, CA, USA; Department of Neurosurgery, University of California, San Francisco, CA, USA; Institute for Computational Health Sciences, University of California, San Francisco, CA, USA; Affiliate, UCB/UCSF Graduate Group in Bioengineering, University of California, San Francisco, CA, USA.
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D056902:Clinical Alarms; D003422:Critical Care / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003951:Diagnostic Errors / Q000517:prevention & control*; D004562:Electrocardiography / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D007362:Intensive Care Units; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017180:Tachycardia, Ventricular / Q000175:diagnosis*; D058067:Wavelet Analysis
keywords: Alarm fatigue; Cardiac monitor alarms; Classification; False alarm reduction; Feature extraction; Multiresolution wavelet; Transform; Ventricular tachycardia