ArticlePloS one2020
Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 38 citations in OpenAlex.
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- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
- Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases.Journal of personalized medicine · 2023Article
- Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.Sensors (Basel, Switzerland) · 2022Review
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- A contrastive learning approach for ICU false arrhythmia alarm reduction.Scientific reports · 2022Article
- ECG Language processing (ELP): A new technique to analyze ECG signals.Computer methods and programs in biomedicine · 2021Article
- A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection.Computers in biology and medicine · 2021Article
- Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records.Computer methods and programs in biomedicine · 2020Article
- HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks.Computers in biology and medicine · 2020Article
- A Graph-constrained Changepoint Detection Approach for ECG Segmentation.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.