{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T17:45:11Z","timestamp":1790876711513,"version":"4.1.0"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/diagnostics12081794","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T04:52:47Z","timestamp":1658724767000},"page":"1794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["Machine Learning for Anxiety Detection Using Biosignals: A Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Lou","family":"Ancillon","sequence":"first","affiliation":[{"name":"Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, Switzerland"},{"name":"Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Elgendi","sequence":"additional","affiliation":[{"name":"Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Menon","sequence":"additional","affiliation":[{"name":"Biomedical and Mobile Health Technology Lab, ETH Zurich, 8008 Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2017). Depression and Other Common Mental Disorders: Global Health Estimates, World Health Organization."},{"key":"ref_2","unstructured":"Cai, Z. (2012). Study of Event-Related fMRI in Generalized Anxiety Sisorder with Negative Emotion Suppression Sisorder and Negative Emotion Initiation, Shantou University."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1027\/0269-8803\/a000244","article-title":"Mindfulness meditation intervention alters neurophysiological symptoms of anxiety and depression in preadolescents","volume":"34","author":"Shanok","year":"2019","journal-title":"J. Psycho-Physiol."},{"key":"ref_4","first-page":"2","article-title":"Diagnosis and management of anxiety disorders","volume":"104","author":"Shelton","year":"2004","journal-title":"J. Am. Osteopath. Assoc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1037\/bul0000128","article-title":"Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories","volume":"144","author":"Siegel","year":"2018","journal-title":"Psychol. Bull."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ijpsycho.2012.10.012","article-title":"Craske Heart rate and heart rate variability in panic, social anxiety, obsessive\u2013compulsive, and generalized anxiety disorders at baseline and in response to relaxation and hyperventilation","volume":"87","author":"Pittig","year":"2013","journal-title":"Int. J. Psychophysiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1017\/S0025727300028350","article-title":"The History of The Electrical Activity of The Brain as A Method for Localizing Sensory Function","volume":"7","author":"Brazier","year":"1963","journal-title":"Med. Hist."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10803-019-04232-5","article-title":"A behavioural assessment of social anxiety and social motivation in fragile X, cornelia de lange and rubinstein-taybi syndromes","volume":"50","author":"Crawford","year":"2020","journal-title":"J. Autism Dev. Disord."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"173","DOI":"10.2307\/1422117","article-title":"Cognitive Psychophysiology: Principles of Covert Behavior","volume":"93","author":"Cacioppo","year":"1980","journal-title":"Am. J. Psychol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s40846-019-00468-9","article-title":"Evaluation of coherence between ECG and PPG derived parameters on heart rate variability and respiration in healthy volunteers with\/without controlled breathing","volume":"39","author":"Jan","year":"2019","journal-title":"J. Med. Biol. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"258","DOI":"10.3389\/fpubh.2017.00258","article-title":"An Overview of Heart Rate Variability Metrics and Norms","volume":"5","author":"Shaffer","year":"2017","journal-title":"Front. Public Health"},{"key":"ref_12","first-page":"404","article-title":"Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability","volume":"21","author":"Baek","year":"2015","journal-title":"Telemed. J. e-Health Off. J. Am. Telemed. Assoc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"594880","DOI":"10.3389\/fnins.2020.594880","article-title":"A Critical review of ultra-short-term heart rate variability norms research","volume":"14","author":"Shaffer","year":"2020","journal-title":"Front. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Castaldo, R., Montesinos, L., Melillo, P., James, C., and Pecchia, L. (2019). Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0742-y"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ihmig, F.R., Neurohr-Parakenings, F., Sch\u00e4fer, S.K., Lass-Hennemann, J., and Michael, T. (2020). On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231517"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vulpe-Grigora\u0219i, A., and Grigore, O. (2021, January 18\u201319). A Neural Network Approach for Anxiety Detection Based on ECG. Proceedings of the 2021 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania.","DOI":"10.1109\/EHB52898.2021.9657544"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1109\/TAFFC.2019.2927337","article-title":"Review on psychological stress detection using biosignals","volume":"13","author":"Giannakakis","year":"2019","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"McDuff, D., Gontarek, S., and Picard, R. (2014, January 26\u201330). Remote measurement of cognitive stress via heart rate variability. Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA.","DOI":"10.1109\/EMBC.2014.6944243"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1111\/j.1469-8986.1983.tb02156.x","article-title":"Respiration, stress, and cardiovascular function","volume":"20","author":"Grossman","year":"1983","journal-title":"Psychophysiology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.bbr.2017.12.021","article-title":"Psychological stress level detection based on electrodermal activity","volume":"341","author":"Liu","year":"2018","journal-title":"Behav. Brain Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"730","DOI":"10.3389\/fpsyg.2020.00730","article-title":"Review of EEG, ERP, and brain connectivity estimators as predictive biomarkers of social anxiety disorder","volume":"11","author":"Kamel","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.nec.2010.11.001","article-title":"Glover Overview of Functional Magnetic Resonance Imaging","volume":"22","author":"Gary","year":"2011","journal-title":"Neurosurg. Clin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106314","DOI":"10.1016\/j.cmpb.2021.106314","article-title":"Machine Learning for personalized stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response","volume":"209","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1109\/JBHI.2014.2311044","article-title":"Cluster-Based Analysis for Personalized Stress Evaluation Using Physiological Signals","volume":"19","author":"Xu","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"86899","DOI":"10.1109\/ACCESS.2021.3089358","article-title":"Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity","volume":"9","author":"Yahya","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e10448","DOI":"10.7717\/peerj.10448","article-title":"Prediction of state anxiety by machine learning applied to photoplethysmography data","volume":"9","author":"Perpetuini","year":"2021","journal-title":"PeerJ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105408","DOI":"10.1016\/j.cmpb.2020.105408","article-title":"Towards an anxiety and stress recognition system for academic environments based on physiological features","volume":"190","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s40846-020-00596-7","article-title":"EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System","volume":"41","author":"Chen","year":"2021","journal-title":"J. Med. Biol. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gazi, A.H., Lis, P., Mohseni, A., Ompi, C., Giuste, F.O., Shi, W., Inan, O.T., and Wang, M.D. (2021, January 27\u201330). Respiratory Markers Significantly Enhance Anxiety Detection Using Multimodal Physiological Sensing. Proceedings of the 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Athens, Greece.","DOI":"10.1109\/BHI50953.2021.9508589"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Selzler, R., Chan, A.D., and Green, J.R. (2021, January 23\u201325). Green Impact of Subject-specific Training Data in Anxiety Level Classification from Physiologic Data. Proceedings of the 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lausanne, Switzerland.","DOI":"10.1109\/MeMeA52024.2021.9478757"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1007\/s10916-016-0551-0","article-title":"Naturalistic Enactment to Elicit and Recognize Caregiver State Anxiety","volume":"40","author":"Miranda","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xie, Y., Yang, B., Lu, X., Zheng, M., Fan, C., Bi, X., and Li, Y. (2020, January 20\u201324). Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176471"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102053","DOI":"10.1109\/ACCESS.2021.3097038","article-title":"The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment","volume":"9","author":"Aristizabal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Muhammad, F., and Al-Ahmadi, S. (2022). Human state anxiety classification framework using EEG signals in response to exposure therapy. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0265679"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1650041","DOI":"10.1142\/S0129065716500416","article-title":"Stress detection using wearable physiological and sociometric sensors","volume":"27","author":"Mozos","year":"2017","journal-title":"Int. J. Neural Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1159\/000119004","article-title":"The \u2018Trier Social Stress Test\u2019\u2014A tool for investigating psychobiological stress responses in a laboratory setting","volume":"28","author":"Selzler","year":"1993","journal-title":"Neuropsychobiology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1001\/archpsyc.62.6.617","article-title":"Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication","volume":"62","author":"Kessler","year":"2005","journal-title":"Arch. Gen. Psychiatry"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/8\/1794\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:51Z","timestamp":1760140551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/8\/1794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":37,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["diagnostics12081794"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12081794","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,25]]}}}