Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine Learning Approach

2025/03/18 by Grace Funmilayo Farayola, Akinyemi Sadeeq Akintola, Farayola, Grace Funmilayo +20
Medicine · Neuroscience · #Artificial Intelligence (cs.AI) #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Healthcare Technology and Patient Monitoring #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2503.14621

openalex publication_date 2025/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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