ArticlePloS one2020
On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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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
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 85 citations in OpenAlex.
- Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Biofeedback-Based Connected Mental Health Interventions for Anxiety: Systematic Literature Review.JMIR mHealth and uHealth · 2021Pooled it
- The Use of Virtual Therapy in Cardiac Rehabilitation of Female Patients with Heart Disease.Medicina (Kaunas, Lithuania) · 2021Trial
- Wearable ECG and PPG for anxiety detection: a translational digital medicine perspective.NPJ digital medicine · 2026Article
- Wearable devices for anxiety assessment: a systematic review.Communications medicine · 2026Article
- Baseline normalization choices inflate classification performance in wearable health monitoring: quantification and mitigation strategies.Frontiers in digital health · 2026Article
- Developing personalized algorithms for sensing mental health symptoms in daily life.Npj mental health research · 2025Article
- A novel approach to anxiety level prediction using small sets of judgment and survey variables.Npj mental health research · 2024Article
- Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features.Sensors (Basel, Switzerland) · 2024Article
- Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions.Frontiers in psychiatry · 2024Article
- Using iVR to deliver optimal psychotherapy experience-current perspectives on VRET for acrophobia.Frontiers in psychology · 2024Article
- Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Wearables measuring electrodermal activity to assess perceived stress in care: a scoping review.Acta neuropsychiatrica · 2023Review
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
- Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.Journal of medical Internet research · 2023Article
- Transcutaneous auricular Vagus Nerve Stimulation and Median Nerve Stimulation reduce acute stress in young healthy adults: a single-blind sham-controlled crossover study.Frontiers in neuroscience · 2023Article
- Predicting obsessive-compulsive disorder episodes in adolescents using a wearable biosensor-A wrist angel feasibility study.Frontiers in psychiatry · 2023Article
- Utility of the Full ECG Waveform for Stress Classification.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning for Anxiety Detection Using Biosignals: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- Human state anxiety classification framework using EEG signals in response to exposure therapy.PloS one · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spider-fearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia.
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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.