ReviewDiagnostics (Basel, Switzerland)2022
Machine Learning for Anxiety Detection Using Biosignals: A Review.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence for the Analysis of Biometric Data from Wearables in Education: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Machine learning approaches to anxiety detection: trends, model evaluation, and future directions.Frontiers in artificial intelligence · 2025Pooled it
- Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Investigation of Machine Learning Models for Anxiety Levels Assessment in Patients With Breast Cancer Using Self-Questionnaires.Journal of Korean medical science · 2026Article
- Perioperative Anxiety in Adults: A Narrative Review of Pathophysiology, Assessment, and Multimodal Management Strategies.Healthcare (Basel, Switzerland) · 2026Review
- Multimodal Detection of Pain and Anticipation Anxiety from Ultra-Short Duration Wearable Sensors Measurements.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence in the Assessment of Heart Rate Variability as an Instrument to Understand the Connection Between Psychologic and Psychiatric Conditions and the Heart.Bioengineering (Basel, Switzerland) · 2026Review
- Exploratory analysis of machine learning models for state and trait anxiety based on Spielberger questionnaire data in nursing students.BMC medical education · 2026Article
- Artificial Intelligence-Based Depression Detection.Sensors (Basel, Switzerland) · 2026Article
- Baseline normalization choices inflate classification performance in wearable health monitoring: quantification and mitigation strategies.Frontiers in digital health · 2026Article
- A novel deep learning model for objective quantification of generalized anxiety disorder severity using EEG functional connectivity.Frontiers in psychiatry · 2026Article
- Closed-loop EEG-based neurofeedback and brain-computer interface interventions for mental health: a review.Frontiers in neuroscience · 2026Review
- EEG entropy modulation as a biomarker of emotion regulation and resilience.IBRO neuroscience reports · 2025Review
- Extending Anxiety Detection from Multimodal Wearables in Controlled Conditions to Real-World Environments.Sensors (Basel, Switzerland) · 2025Article
- Pattern Identification in Patients with Functional Dyspepsia Using Brain-Body Bio-Signals: Protocol of a Clinical Trial for AI Algorithm Development.Journal of clinical medicine · 2025Article
- Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach.Diagnostics (Basel, Switzerland) · 2024Article
- Wearable neurofeedback acceptance model for students' stress and anxiety management in academic settings.PloS one · 2024Article
- A review on the efficacy of artificial intelligence for managing anxiety disorders.Frontiers in artificial intelligence · 2024Review
- Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training.Applied sciences (Basel, Switzerland) · 2023Article
- Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anxiety disorder (AD) is a major mental health illness. However, due to the many symptoms and confounding factors associated with AD, it is difficult to diagnose, and patients remain untreated for a long time. Therefore, researchers have become increasingly interested in non-invasive biosignals, such as electroencephalography (EEG), electrocardiogram (ECG), electrodermal response (EDA), and respiration (RSP). Applying machine learning to these signals enables clinicians to recognize patterns of anxiety and differentiate a sick patient from a healthy one. Further, models with multiple and diverse biosignals have been developed to improve accuracy and convenience. This paper reviews and summarizes studies published from 2012 to 2022 that applied different machine learning algorithms with various biosignals. In doing so, it offers perspectives on the strengths and weaknesses of current developments to guide future advancements in anxiety detection. Specifically, this literature review reveals promising measurement accuracies ranging from 55% to 98% for studies with sample sizes of 10 to 102 participants. On average, studies using only EEG seemed to obtain the best performance, but the most accurate results were obtained with EDA, RSP, and heart rate. Random forest and support vector machines were found to be widely used machine learning methods, and they lead to good results as long as feature selection has been performed. Neural networks are also extensively used and provide good accuracy, with the benefit that no feature selection is needed. This review also comments on the effective combinations of modalities and the success of different models for detecting anxiety.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.