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An Application for Pairing with Wearable Devices to Monitor Personal Health Status

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Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data.

Mahnoosh Sadeghi¹, Anthony D McDonald¹, Farzan Sasangohar¹

¹Department of Industrial and / Systems Engineering, Texas A&M University, College Station, Texas, United States of America.

|May 18, 2022

Summary

This summary is machine-generated.

This study developed machine learning algorithms to detect hyperarousal events in veterans with Posttraumatic Stress Disorder (PTSD). The XGBoost model achieved over 83% accuracy, paving the way for continuous PTSD monitoring and timely interventions.

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Area of Science:

Psychiatry
Computer Science
Biomedical Engineering

Background:

Posttraumatic Stress Disorder (PTSD) affects nearly a quarter of US war veterans, with severe symptoms often occurring outside clinical settings.
Current mobile health interventions for PTSD lack continuous monitoring and timely detection capabilities.
There is a critical need for effective remote monitoring systems to support PTSD self-management.

Purpose of the Study:

To develop and evaluate novel machine learning algorithms for detecting hyperarousal events in individuals with PTSD.
To leverage physiological and activity data from wearable devices for continuous PTSD symptom monitoring.
To identify key physiological indicators associated with PTSD hyperarousal episodes.

Main Methods:

Collected physiological data (heart rate, body acceleration) and self-reported hyperarousal events from 99 US veterans with PTSD using wearable devices.
Developed and compared four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression, and XGBoost.
Utilized SHapley Additive exPlanations (SHAP) for post-hoc analysis of algorithm predictions.

Main Results:

The XGBoost model demonstrated the highest performance in detecting the onset of PTSD symptoms, achieving over 83% accuracy and an AUC of 0.70.
SHAP analysis revealed that average heart rate, minimum heart rate, and average body acceleration were significant predictors in the XGBoost model.
The findings indicate a promising approach for the continuous, remote detection of PTSD symptom onset.

Conclusions:

Machine learning algorithms, particularly XGBoost, show significant promise for detecting hyperarousal events indicative of PTSD symptom onset.
The developed method can form the basis for remote, continuous monitoring systems for PTSD, enabling just-in-time interventions.
This technology has the potential to significantly improve PTSD self-management and clinical care for veterans outside of traditional appointments.