ArticlePeerJ2021
Prediction of state anxiety by machine learning applied to photoplethysmography data.
Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Machine learning approaches to anxiety detection: trends, model evaluation, and future directions.Frontiers in artificial intelligence · 2025Pooled it
- A Survey on Wearable Sensors for Mental Health Monitoring.Sensors (Basel, Switzerland) · 2023Pooled it
- 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
- Extending Anxiety Detection from Multimodal Wearables in Controlled Conditions to Real-World Environments.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Detection Method of Atrial Fibrillation by CEPNCC-BiLSTM Based on Long-Term Photoplethysmography Data.Sensors (Basel, Switzerland) · 2024Article
- A novel approach to anxiety level prediction using small sets of judgment and survey variables.Npj mental health research · 2024Article
- Assessing the Impact of Electrosuit Therapy on Cerebral Palsy: A Study on the Users' Satisfaction and Potential Efficacy.Brain sciences · 2023Article
- Assessing Feasibility of Cognitive Impairment Testing Using Social Robotic Technology Augmented with Affective Computing and Emotional State Detection Systems.Biomimetics (Basel, Switzerland) · 2023Article
- A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG.Diagnostics (Basel, Switzerland) · 2023Article
- The Use of Photoplethysmography in the Assessment of Mental Health: Scoping Review.JMIR mental health · 2023Article
- Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue?Sensors (Basel, Switzerland) · 2023Article
- Editorial: Effect of neurophysiological conditions and mental workload on physical and cognitive performances: a multidimensional perspective.Frontiers in neuroergonomics · 2023Article
- Article
- Machine Learning for Anxiety Detection Using Biosignals: A Review.Diagnostics (Basel, Switzerland) · 2022Review
- Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach.Sensors (Basel, Switzerland) · 2022Article
- Trends in Daily Heart Rate Variability Fluctuations Are Associated with Longitudinal Changes in Stress and Somatisation in Police Officers.Healthcare (Basel, Switzerland) · 2022Article
- Human state anxiety classification framework using EEG signals in response to exposure therapy.PloS one · 2022Article
- Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated.
methodsThe machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test.
resultsA leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (
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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.