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Towards a Lightweight Classifier to Detect Hypovolemic Shock.

Leena Pramanik, Christopher L Felton, Robert W Techentin

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

Summary

This study introduces a simple predictor for compensatory reserve metric (CRM) to identify hypovolemic shock risk. The model is suitable for real-time monitoring on wearable devices, aiding early clinical intervention during blood loss.

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Area of Science:

Biomedical Engineering
Physiology
Machine Learning in Healthcare

Background:

Early detection of hypovolemic shock is critical for timely clinical intervention.
The compensatory reserve metric (CRM) quantifies the body's ability to manage blood loss.

Purpose of the Study:

To develop a lightweight, three-class predictor for CRM.
To assess the feasibility of real-time hypovolemia monitoring using a wearable device.

Main Methods:

Utilized a linear support-vector machine (SVM) classifier for prediction.
Employed a moving mean filter for smoothing predictions.
Focused on a three-class classification: good, fair, and poor compensatory reserve.

Main Results:

Developed a predictor for CRM with good, fair, and poor classifications.
Demonstrated a feasible model for real-time hypovolemia monitoring.
The model requires minimal storage (408 bytes) and computational resources.

Conclusions:

The developed SVM classifier provides a feasible approach for real-time hypovolemia monitoring.
The lightweight nature of the model supports implementation on wearable devices.
Early detection of declining compensatory reserve can facilitate timely medical intervention.