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

arXiv: https://arxiv.org/abs/2503.14621 · HTML: https://mlsanity.com/paper/2503.14621/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)

## Built on by (most cited first, 1 of 1)

- [Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU](https://mlsanity.com/paper/2609.08992/) (2026)

## Abstract (arXiv)

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.