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Multi-level Pain Quantification using a Smartphone and Electrodermal Activity.

Youngsun Kong, Hugo F Posada-Quintero, Ki H Chon

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a smartphone app using electrodermal activity (EDA) sensors for objective, multi-level pain assessment. The wearable device shows potential for real-time ambulatory pain monitoring, aiding chronic pain management.

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    Area of Science:

    • Biomedical Engineering
    • Physiological Measurement
    • Digital Health

    Background:

    • Pain assessment is subjective, relying on patient self-reports, contributing to the opioid crisis.
    • Objective pain measurement is needed for appropriate pain management and to combat addiction.
    • Ambulatory pain monitoring is desirable for continuous pain assessment.

    Purpose of the Study:

    • To evaluate a smartphone application using electrodermal activity (EDA) for multi-level pain detection.
    • To test the feasibility of real-time ambulatory pain monitoring using a wearable EDA device.
    • To address the unmet need for objective pain assessment tools.

    Main Methods:

    • Developed a smartphone application linked via Bluetooth to an EDA wearable device.
    • Collected multi-level pain data from 15 subjects exposed to electrical pulse stimuli.
    • Applied statistical analyses and machine learning (random forest) for pain level classification.

    Main Results:

    • Significant differences in EDA-derived indices were found across no-pain, low-pain, and high-pain states.
    • A random forest classifier achieved 62.6% balanced accuracy for multi-level pain detection.
    • A random forest regressor demonstrated a coefficient of determination of 0.441.

    Conclusions:

    • The smartphone application with an EDA wearable device demonstrates feasibility for detecting multiple pain levels.
    • This technology offers potential for objective, real-time ambulatory pain monitoring.
    • The system could be valuable for managing chronic pain conditions.