Log In Sign Up

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

by   Sajad Mousavi, et al.

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 92.05 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 specificity of 88.30 Tachycardia arrhythmia)


page 1

page 3

page 4


Deep transfer learning for system identification using long short-term memory neural networks

Recurrent neural networks (RNNs) have many advantages over more traditio...

DENS-ECG: A Deep Learning Approach for ECG Signal Delineation

Objectives: With the technological advancements in the field of tele-hea...

Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

A novel and efficient end-to-end learning model for automatic modulation...

A Robust Deep Learning Approach for Automatic Seizure Detection

Detecting epileptic seizure through analysis of the electroencephalograp...

An Unsupervised Feature Learning Approach to Reduce False Alarm Rate in ICUs

The high rate of false alarms in intensive care units (ICUs) is one of t...

A Shapley Value Solution to Game Theoretic-based Feature Reduction in False Alarm Detection

False alarm is one of the main concerns in intensive care units and can ...