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

Autoři: Sajad Mousavi aff001;  Atiyeh Fotoohinasab aff001;  Fatemeh Afghah aff001
Působiště autorů: School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America aff001
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0226990


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).

Klíčová slova:

Algorithms – Cardiology – Electrocardiography – Machine learning algorithms – Deep learning – Heart rate – Arrhythmia – Tachycardia


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