Journal Article

Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks

Fotoohinasab, Atiyeh, Afghah, Fatemeh, Mousavi, Sajad

2020

Overview

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 multi- modal 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 segmented input 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).

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Alarms / Algorithms / Arrhythmia / Arrhythmias, Cardiac - diagnosis / Artificial neural networks / Cardiac arrhythmia / Cardiology / Clinical Alarms - standards / Computer and Information Sciences / Datasets / Deep learning / EKG / Electrocardiography / False alarms / False Positive Reactions / Feature extraction / Heart rate / Hospitals / Humans / Informatics / Intensive Care Units / Learning / Learning algorithms / Long short-term memory / Machine learning / Medicine and Health Sciences / Monitoring, Physiologic - instrumentation / Monitoring, Physiologic - methods / Monitoring, Physiologic - standards / Neural networks / Neural Networks, Computer / Patients / Physical Sciences / Principal components analysis / Recurrent neural networks / Reduction / Research and Analysis Methods / Sensitivity / Sensitivity and Specificity / Short term memory / Supervised Machine Learning - standards / Tachycardia / Teaching methods / Training / Ventricle