An automated ICU agitation monitoring system for video streaming using deep learning classification.

ABSTRACT:

Objective

To 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.

Methods

We 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.

Results

The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction.

SUBMITTER: Dai PY 

PROVIDER: S-EPMC10946151 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature