ArticleBMC medical informatics and decision making2024
An automated ICU agitation monitoring system for video streaming using deep learning classification.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Research progress in agitation early warning monitoring and new technologies for critically ill patients: a narrative review.Frontiers in medicine · 2026Review
- Image recognition-based detection system for preventing accidental dislodgement of head-and-neck medical supplies in ICU patients: A feasibility randomized controlled trial.International journal of nursing sciences · 2026Article
- Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model.JMIR medical informatics · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
objectiveTo address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning.
methodsWe collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as "Attention" and "Non-attention". After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances.
resultsThe video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods.
conclusionOur study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application.
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Registered trials
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