Year: 2025
Type: conference-paper
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.
Authors: Grace Funmilayo Farayola, Akinyemi Sadeeq Akintola, Oluwole Fagbohun, Chukwuka Michael Oforgu, Bisola Faith Kayode, Christian Chimezie, Temitope Kadri, Abiola Oludotun, Nelson Qobeide, Mgbame Michael, Adeseye Ifaturoti, Toyese Oloyede
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4415125451