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Sedation Evaluation Based on Support Vector Machines Using IMU Data
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AI
DOI：10.1109/JSEN.2024.3387407.png)
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Abstract
En 中文
An escalated level of sedation has been associated with adverse outcomes, including mortality, delirium, and delayed extubation. Hence, maintaining a sedation level corresponding to a Richmond agitation-sedation scale (RASS) value of 0 is paramount in clinical settings. This article presents an innovative approach to sedation assessment, aiming to administer a minimal sedative dose while accounting for the patient's 3-D movements. Data acquisition was carried out utilizing an inertial measurement unit (IMU), with real-time evaluation of patient RASS values achieved through support vector machine (SVM) classification. Five SVM classifiers were developed to distinguish RASS levels 0-4. Test outcomes showcased an average accuracy rate of 91.30%. Particularly noteworthy was the attainment of a 94.40% accuracy rate when the RASS value was 0.
Keywords:
Dosing
inertial measurement unit (IMU)
Richmond agitation-sedation scale (RASS)
sedation
support vector machine (SVM)