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Related Experiment Video

Updated: Jun 30, 2026

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Using the Threat Probability Task to Assess Anxiety and Fear During Uncertain and Certain Threat

Published on: September 12, 2014

A Systematic Review on Physiology-based Anxiety Detection using Machine Learning.

Shikha Shikha¹, Divyashikha Sethia², S Indu³

¹Computer Science and engineering, Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi-110042, New Delhi, New Delhi, Delhi, 110042, INDIA.

Biomedical Physics & Engineering Express

|May 9, 2025

Summary

This summary is machine-generated.

This review explores using machine learning with physiological signals like electroencephalography to detect anxiety disorder. It highlights wearable devices and proposes a multimodal approach for better anxiety classification.

Keywords:

Deep Learning Machine Learning Physiological signals Stress and Anxiety Wearable Sensors

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Last Updated: Jun 30, 2026

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Published on: September 12, 2014

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Area of Science:

Neuroscience and Computational Psychiatry
Focuses on the intersection of brain activity, physiological responses, and artificial intelligence for mental health diagnostics.

Background:

Anxiety disorder diagnosis is challenging due to complex symptoms, leading to delayed treatment.
Non-invasive physiological signals offer a promising avenue for objective anxiety assessment.

Purpose of the Study:

To systematically review physiological sensors and machine learning (ML) for anxiety disorder diagnosis and prediction.
To explore the relationship between physiological features and anxiety using ML models.
To propose a novel multimodal approach for enhanced anxiety classification.

Main Methods:

Systematic literature review of physiological sensors (EEG, ECG, EMG, EDA, respiration) and ML techniques.
Analysis of wearable devices used in anxiety detection studies.
Exploration of ML model performance in correlating physiological data with anxiety.

Main Results:

Machine learning effectively identifies anxiety patterns from physiological signals.
Wearable devices are increasingly utilized for remote and continuous anxiety monitoring.
A multimodal approach combining various physiological signals shows potential for improved accuracy.

Conclusions:

ML integration with physiological signals offers a viable path for objective anxiety detection.
Further research is needed to address challenges in data standardization and model generalizability.
The proposed multimodal strategy could significantly advance anxiety disorder diagnosis and management.