Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine Learning Approach
This work addresses a critical issue for ICU patients and staff by reducing false alarms, though it is incremental as it applies existing deep learning methods to a specific medical dataset.
The paper tackled the problem of false ventricular tachycardia (VT) alarms in ICUs, which cause alarm fatigue and safety risks, by developing a machine learning approach that achieved ROC-AUC scores over 0.96 for accurate classification.
False arrhythmia alarms in intensive care units (ICUs) are a significant challenge, contributing to alarm fatigue and potentially compromising patient safety. Ventricular tachycardia (VT) alarms are particularly difficult to detect accurately due to their complex nature. This paper presents a machine learning approach to reduce false VT alarms using the VTaC dataset, a benchmark dataset of annotated VT alarms from ICU monitors. We extract time-domain and frequency-domain features from waveform data, preprocess the data, and train deep learning models to classify true and false VT alarms. Our results demonstrate high performance, with ROC-AUC scores exceeding 0.96 across various training configurations. This work highlights the potential of machine learning to improve the accuracy of VT alarm detection in clinical settings.