# Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1909.11791 · HTML: https://mlsanity.com/paper/1909.11791/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients](https://mlsanity.com/paper/2509.06516/) (2025)
- [A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection](https://mlsanity.com/paper/2102.01319/) (2021)
- [Deep conv-attention model for diagnosing left bundle branch block from 12-lead electrocardiograms](https://mlsanity.com/paper/2212.04936/) (2022)
- [A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection](https://mlsanity.com/paper/2102.03538/) (2021)
- [SigmaMedStat: Temporal Signal Modeling for ICU False Alarm Reduction](https://mlsanity.com/paper/2605.29236/) (2026)

## Abstract (arXiv)

This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimodal biosignals. Most of the current work in the literature are either rule-based methods, requiring prior knowledge of arrhythmia analysis to build rules, or classical machine learning approaches, depending on hand-engineered features. In this work, we apply convolutional neural networks to automatically extract time-invariant features, an attention mechanism to put more emphasis on the important regions of the input segmented signal(s) that are more likely to contribute to an alarm, and long short-term memory units to capture the temporal information presented in the signal segments. We trained our method efficiently using a two-step training algorithm (i.e., pre-training and fine-tuning the proposed network) on the dataset provided by the PhysioNet computing in cardiology challenge 2015. The evaluation results demonstrate that the proposed method obtains better results compared to other existing algorithms for the false alarm reduction task in ICUs. The proposed method achieves a sensitivity of 93.88% and a specificity of 92.05% for the alarm classification, considering three different signals. In addition, our experiments for 5 separate alarm types leads significant results, where we just consider a single-lead ECG (e.g., a sensitivity of 90.71%, a specificity of 88.30%, an AUC of 89.51 for alarm type of Ventricular Tachycardia arrhythmia)