False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal

• Published in: Journal of electrocardiology Vol. 47; no. 6; pp. 775 - 780
• Main Authors: Salas-Boni, Rebeca, PhD, Bai, Yong, MS, Harris, Patricia Rae Eileen, PhD, RN, Drew, Barbara J., RN, PhD, FAAN, FAHA, Hu, Xiao, PhD
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2014
• Subjects: Alarm fatigue; Algorithms; Cardiac monitor alarms; Cardiovascular; Classification; Clinical Alarms; Critical Care - methods; Diagnosis, Computer-Assisted - methods; Diagnostic Errors - prevention & control; Electrocardiography - methods; False alarm reduction; False Positive Reactions; Feature extraction; Humans; Intensive Care Units; Multiresolution wavelet; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Tachycardia, Ventricular - diagnosis; Transform; Ventricular tachycardia; Wavelet Analysis; False alarm reduction; Classification; Cardiac monitor alarms; Alarm fatigue; Ventricular tachycardia; Feature extraction; Multiresolution wavelet; Transform
• DOI: 10.1016/j.jelectrocard.2014.07.016
• ISSN: 0022-0736

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 20 seconds 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.