False arrhythmia alarm reduction in the intensive care unit

TL;DR

This paper presents a machine learning-based system to significantly reduce false arrhythmia alarms in the ICU, improving patient safety and caregiver response by accurately distinguishing true from false alarms.

Contribution

It introduces a robust, signal processing and machine learning-driven algorithm that enhances false alarm detection for multiple arrhythmia types in the ICU setting.

Findings

Sensitivity of 0.908 achieved

Specificity of 0.838 achieved

PhysioNet/CinC challenge score of 0.756

Abstract

Research has shown that false alarms constitute more than 80% of the alarms triggered in the intensive care unit (ICU). The high false arrhythmia alarm rate has severe implications such as disruption of patient care, caregiver alarm fatigue, and desensitization from clinical staff to real life-threatening alarms. A method to reduce the false alarm rate would therefore greatly benefit patients as well as nurses in their ability to provide care. We here develop and describe a robust false arrhythmia alarm reduction system for use in the ICU. Building off of work previously described in the literature, we make use of signal processing and machine learning techniques to identify true and false alarms for five arrhythmia types. This baseline algorithm alone is able to perform remarkably well, with a sensitivity of 0.908, a specificity of 0.838, and a PhysioNet/CinC challenge score of 0.756. We additionally explore dynamic time warping techniques on both the entire alarm signal as well as on a beat-by-beat basis in an effort to improve performance of ventricular tachycardia, which has in the literature been one of the hardest arrhythmias to classify. Such an algorithm with strong performance and efficiency could potentially be translated for use in the ICU to promote overall patient care and recovery.