Exploring Diverse Features: A Thorough Survey for Anxiety Disorders
DOI:
https://doi.org/10.4108/eetpht.11.5475Keywords:
Anxiety Disorder, Mental Illness, Acoustic features, Disorder IdentificationAbstract
INTRODUCTION: The prevalence of mental health issues, mainly anxiety disorders, has risen significantly in today’s fast paced world. Thus, giving the imperative to confront the challenges associated in identifying these issues.
OBJECTIVE: The objective of this review paper is to signify the importance of different features or parameters (acoustic, prosodic, linguistic, facial, neuroimaging, psychological, and physiological) being extracted from different data modalities (audio, video, psychological, physiological, textual, and neuroimaging) that are being used to assess different kinds of mental disorders.
METHODS: Considering the systematic literature review technique, a total of 102 studies have been identified in the field of anxiety disorders and mental health issues spanning the years 2015 to 2024. By considering diverse features being used to diagnose different kinds of anxiety disorders, this paper provides a foundation for future research that will help researchers to design the new strategies and techniques to handle the anxiety disorder.
CONCLUSION: This comprehensive review paper outlines the details of diverse features extracted across various data modalities, contributing significantly to the prediction of a wide range of anxiety disorders.
Downloads
References
[1] Trautmann S, Rehm J, Wittchen H. The economic costs of mental disorders. EMBO Rep 2016;17:1245–9. https://doi.org/10.15252/EMBR.201642951.
[2] Christensen MK, Lim CCW, Saha S, Plana-Ripoll O, Cannon D, Momen NC, Whiteford HA, Iburg KM, McGrath JJ. The cost of mental disorders: a systematic review. Epidemiol Psychiatr Sci 2020;29:e161. https://doi.org/10.1017/S204579602000075X.
[3] Ahmed A, Sultana R, Ullas MTR, Begom M, Rahi MMI, Alam MA. A Machine Learning Approach to detect Depression and Anxiety using Supervised Learning. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020, Institute of Electrical and Electronics Engineers Inc.; 2020, p. 1–6. https://doi.org/10.1109/CSDE50874.2020.9411642.
[4] Craske MG, Stein MB. Anxiety. The Lancet 2016;388:3048–59. https://doi.org/10.1016/S0140-6736(16)30381-6.
[5] Malgaroli M, Hull TD, Calderon A, Simon NM. Linguistic markers of anxiety and depression in Somatic Symptom and Related Disorders: Observational study of a digital intervention. J Affect Disord 2024;352:133–7. https://doi.org/10.1016/J.JAD.2024.02.012.
[6] Thibaut F. Anxiety disorders: a review of current literature. Https://DoiOrg/1031887/DCNS2017192/Fthibaut 2022;19:87–8. https://doi.org/10.31887/DCNS.2017.19.2/FTHIBAUT.
[7] Pediaditis M, Giannakakis G, Chiarugi F, Manousos D, Pampouchidou A, Christinaki E, Iatraki G, Kazantzaki E, Simos PG, Marias K, Tsiknakis M. Extraction of facial features as indicators of stress and anxiety. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2015:3711–4. https://doi.org/10.1109/EMBC.2015.7319199.
[8] Arif M, Basri A, Melibari G, Sindi T, Alghamdi N, Altalhi N, Arif M. Classification of anxiety disorders using machine learning methods: a literature review. PdfsSemanticscholarOrg 2020. https://doi.org/10.36959/584/455.
[9] López-Morales H, Canet-Juric L, del-Valle MV, Sosa JM, López MC, Urquijo S. Prenatal anxiety during the pandemic context is related to neurodevelopment of 6-month-old babies. Eur J Pediatr 2023;182:4213–26. https://doi.org/10.1007/S00431-023-05112-Y/METRICS.
[10] McDonald AJ, Mew EJ, Hawley NL, Lowe SR. Anticipating the long-term neurodevelopmental impact of the COVID-19 pandemic on newborns and infants: A call for research and preventive policy. J Affect Disord Rep 2021;6:100213. https://doi.org/10.1016/J.JADR.2021.100213.
[11] Davenport MH, McCurdy AP, Mottola MF, Skow RJ, Meah VL, Poitras VJ, Jaramillo Garcia A, Gray CE, Barrowman N, Riske L, Sobierajski F, James M, Nagpal T, Marchand AA, Nuspl M, Slater LG, Barakat R, Adamo KB, Davies GA, Ruchat SM. Impact of prenatal exercise on both prenatal and postnatal anxiety and depressive symptoms: a systematic review and meta-analysis. Br J Sports Med 2018;52:1376–85. https://doi.org/10.1136/BJSPORTS-2018-099697.
[12] Martin P. The epidemiology of anxiety disorders: a review. Dialogues Clin Neurosci 2022;5:281–98. https://doi.org/10.31887/DCNS.2003.5.3/PMARTIN.
[13] Goodwin H, Yiend J, Hirsch CR. Generalized Anxiety Disorder, worry and attention to threat: A systematic review. Clin Psychol Rev 2017;54:107–22. https://doi.org/10.1016/J.CPR.2017.03.006.
[14] Madonna D, Delvecchio G, Soares JC, Brambilla P. Structural and functional neuroimaging studies in generalized anxiety disorder: a systematic review. Brazilian Journal of Psychiatry 2019;41:336. https://doi.org/10.1590/1516-4446-2018-0108.
[15] Priya A, Garg S, Tigga NP. Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. Procedia Comput Sci 2020;167:1258–67. https://doi.org/10.1016/J.PROCS.2020.03.442.
[16] Emmelkamp PMG, Meyerbröker K, Morina N. Virtual Reality Therapy in Social Anxiety Disorder. Curr Psychiatry Rep 2020;22:1–9. https://doi.org/10.1007/S11920-020-01156-1/METRICS.
[17] Sharma A, Verbeke WJMI. Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models. PLoS One 2021;16:e0251365. https://doi.org/10.1371/JOURNAL.PONE.0251365.
[18] Pontillo M, Tata MC, Averna R, Demaria F, Gargiullo P, Guerrera S, Pucciarini ML, Santonastaso O, Vicari S. Peer Victimization and Onset of Social Anxiety Disorder in Children and Adolescents. Brain Sciences 2019, Vol 9, Page 132 2019;9:132. https://doi.org/10.3390/BRAINSCI9060132.
[19] Koyuncu A, İnce E, Ertekin E, Tükel R. Comorbidity in social anxiety disorder: diagnostic and therapeutic challenges. Drugs Context 2019;8. https://doi.org/10.7573/DIC.212573.
[20] Felmingham KL, Stewart LF, Kemp AH, Carr AR. The impact of high trait social anxiety on neural processing of facial emotion expressions in females. Biol Psychol 2016;117:179–86. https://doi.org/10.1016/J.BIOPSYCHO.2016.04.001.
[21] Kleberg JL, Högström J, Sundström K, Frick A, Serlachius E. Delayed gaze shifts away from others’ eyes in children and adolescents with social anxiety disorder. J Affect Disord 2021;278:280–7. https://doi.org/10.1016/J.JAD.2020.09.022.
[22] Kaseda ET, Levine AJ. Post-traumatic stress disorder: A differential diagnostic consideration for COVID-19 survivors. Https://DoiOrg/101080/1385404620201811894 2020;34:1498–514. https://doi.org/10.1080/13854046.2020.1811894.
[23] Li Y, Scherer N, Felix L, Kuper H. Prevalence of depression, anxiety and post-traumatic stress disorder in health care workers during the COVID-19 pandemic: A systematic review and meta-analysis. PLoS One 2021;16:e0246454. https://doi.org/10.1371/JOURNAL.PONE.0246454.
[24] de Jonge P, Roest AM, Lim CCW, Florescu SE, Bromet EJ, Stein DJ, Harris M, Nakov V, Caldas-de-Almeida JM, Levinson D, Al-Hamzawi AO, Haro JM, Viana MC, Borges G, O’Neill S, de Girolamo G, Demyttenaere K, Gureje O, Iwata N, Lee S, Hu C, Karam A, Moskalewicz J, Kovess-Masfety V, Navarro-Mateu F, Browne MO, Piazza M, Posada-Villa J, Torres Y, ten Have ML, Kessler RC, Scott KM. Cross-national epidemiology of panic disorder and panic attacks in the world mental health surveys. Depress Anxiety 2016;33:1155–77. https://doi.org/10.1002/DA.22572.
[25] Na KS, Cho SE, Cho SJ. Machine learning-based discrimination of panic disorder from other anxiety disorders. J Affect Disord 2021;278:1–4. https://doi.org/10.1016/J.JAD.2020.09.027.
[26] Jang EH, Choi KW, Kim AY, Yu HY, Jeon HJ, Byun S. Automated detection of panic disorder based on multimodal physiological signals using machine learning. ETRI Journal 2023;45:105–18. https://doi.org/10.4218/ETRIJ.2021-0299.
[27] Javelot H, Weiner L. Panic and pandemic: Narrative review of the literature on the links and risks of panic disorder as a consequence of the SARS-CoV-2 pandemic. Encephale 2021;47:38–42. https://doi.org/10.1016/J.ENCEP.2020.08.001.
[28] Boudreau M, Lavoie KL, Cartier A, Trutshnigg B, Morizio A, Lemière C, Bacon SL. Do asthma patients with panic disorder really have worse asthma? A comparison of physiological and psychological responses to a methacholine challenge. Respir Med 2015;109:1250–6. https://doi.org/10.1016/J.RMED.2015.09.002.
[29] Bandelow B, Michaelis S. Epidemiology of anxiety disorders in the 21st century. Dialogues Clin Neurosci 2015;17:327–35. https://doi.org/10.31887/DCNS.2015.17.3/BBANDELOW.
[30] Perna G, Iannone G, Alciati A, Caldirola D. Are anxiety disorders associated with accelerated aging? A focus on neuroprogression. Neural Plast 2016;2016. https://doi.org/10.1155/2016/8457612.
[31] Gkotsis G, Oellrich A, Velupillai S, reports ML-S, 2017 undefined. Characterisation of mental health conditions in social media using Informed Deep Learning. NatureCom 2017;7:45141.
[32] Mundel J, Wan A, Yang J. Processes underlying social comparison with influencers and subsequent impulsive buying: The roles of social anxiety and social media addiction. Journal of Marketing Communications 2024;30:834–51. https://doi.org/10.1080/13527266.2023.2183426.
[33] Barak-Corren Y, Castro VM, Javitt S, Hoffnagle AG, Dai Y, Perlis RH, Nock MK, Smoller JW, Reis BY. Predicting suicidal behavior from longitudinal electronic health records. American Journal of Psychiatry 2017;174:154–62. https://doi.org/10.1176/APPI.AJP.2016.16010077/ASSET/IMAGES/LARGE/APPI.AJP.2016.16010077F2.JPEG.
[34] Sharma P, Rosário MC, Ferrão YA, Albertella L, Miguel EC, Fontenelle LF. The impact of generalized anxiety disorder in obsessive-compulsive disorder patients. Psychiatry Res 2021;300:113898. https://doi.org/10.1016/J.PSYCHRES.2021.113898.
[35] Ebrahim OS, Sayed HA, Rabei S, Hegazy N. Perceived stress and anxiety among medical students at Helwan University: A cross-sectional study. J Public Health Res 2024;13:22799036241227892. https://doi.org/10.1177/22799036241227891/ASSET/IMAGES/LARGE/10.1177_22799036241227891-FIG2.JPEG.
[36] Maddock A. The Relationships between Stress, Burnout, Mental Health and Well-Being in Social Workers. The British Journal of Social Work 2024;54:668–86. https://doi.org/10.1093/BJSW/BCAD232.
[37] Robinson LR, Bitsko RH, O’Masta B, Holbrook JR, Ko J, Barry CM, Maher B, Cerles A, Saadeh K, MacMillan L, Mahmooth Z, Bloomfield J, Rush M, Kaminski JW. A Systematic Review and Meta-analysis of Parental Depression, Antidepressant Usage, Antisocial Personality Disorder, and Stress and Anxiety as Risk Factors for Attention-Deficit/Hyperactivity Disorder (ADHD) in Children. Prevention Science 2024;25:272–90. https://doi.org/10.1007/S11121-022-01383-3/METRICS.
[38] Giannakakis G, Pediaditis M, Manousos D, Kazantzaki E, Chiarugi F, Simos PG, Marias K, Tsiknakis M. Stress and anxiety detection using facial cues from videos. Biomed Signal Process Control 2017;31:89–101. https://doi.org/10.1016/J.BSPC.2016.06.020.
[39] Zhang H, Feng L, Li N, Jin Z, Cao L. Video-Based Stress Detection through Deep Learning. Sensors 2020;20:5552. https://doi.org/10.3390/S20195552.
[40] Al-Ezzi A, Yahya N, Kamel N, Faye I, Alsaih K, Gunaseli E. Severity Assessment of Social Anxiety Disorder Using Deep Learning Models on Brain Effective Connectivity. IEEE Access 2021;9:86899–913. https://doi.org/10.1109/ACCESS.2021.3089358.
[41] Paula L dos S, Pfeiffer Salomão Dias L, Francisco R, Barbosa JLV. Analysing IoT Data for Anxiety and Stress Monitoring: A Systematic Mapping Study and Taxonomy. Int J Hum Comput Interact 2024;40:1174–94. https://doi.org/10.1080/10447318.2022.2132361.
[42] Baygin M, Barua PD, Dogan S, Tuncer T, Hong TJ, March S, Tan RS, Molinari F, Acharya UR. Automated anxiety detection using probabilistic binary pattern with ECG signals. Comput Methods Programs Biomed 2024;247:108076. https://doi.org/10.1016/J.CMPB.2024.108076.
[43] Yang T, Wu C, Huang K, Su M. Detection of mood disorder using speech emotion profiles and LSTM. 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), IEEE; 2016, p. 1–5.
[44] Su MH, Wu CH, Huang KY, Yang TH. Cell-Coupled Long Short-Term Memory with L-Skip Fusion Mechanism for Mood Disorder Detection through Elicited Audiovisual Features. IEEE Trans Neural Netw Learn Syst 2020;31:124–35. https://doi.org/10.1109/TNNLS.2019.2899884.
[45] Rejaibi E, Komaty A, Meriaudeau F, Agrebi S, Othmani A. MFCC-based Recurrent Neural Network for automatic clinical depression recognition and assessment from speech. Biomed Signal Process Control 2022;71:103107. https://doi.org/10.1016/J.BSPC.2021.103107.
[46] Tasnim M, Ehghaghi M, … BD-P of the E, 2022 undefined. Depac: a corpus for depression and anxiety detection from speech. AclanthologyOrg 2022:1–16.
[47] Diep B, Stanojevic M, Novikova J. Multi-modal deep learning system for depression and anxiety detection 2022.
[48] Whitfield-Gabrieli S, Ghosh SS, Nieto-Castanon A, Saygin Z, Doehrmann O, Chai XJ, Reynolds GO, Hofmann SG, Pollack MH, Gabrieli JDE. Brain connectomics predict response to treatment in social anxiety disorder. Mol Psychiatry 2015;21:680–5. https://doi.org/10.1038/mp.2015.109.
[49] Salekin A, Eberle JW, Glenn JJ, Teachman BA, Stankovic JA. A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression. Proc ACM Interact Mob Wearable Ubiquitous Technol 2018;2:1–26. https://doi.org/10.1145/3214284.
[50] Rezaei S, Gharepapagh E, Rashidi F, Cattarinussi G, Sanjari Moghaddam H, Di Camillo F, Schiena G, Sambataro F, Brambilla P, Delvecchio G. Machine learning applied to functional magnetic resonance imaging in anxiety disorders. J Affect Disord 2023;342:54–62. https://doi.org/10.1016/J.JAD.2023.09.006.
[51] Bendebane L, Laboudi Z, Saighi A, Al-Tarawneh H, Ouannas A, Grassi G. A Multi-Class Deep Learning Approach for Early Detection of Depressive and Anxiety Disorders Using Twitter Data. Algorithms 2023;16:543. https://doi.org/10.3390/a16120543.
[52] Tasnim M, Ramos RD, Stroulia E, Trejo LA. A Machine-Learning Model for Detecting Depression, Anxiety, and Stress from Speech. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE; 2024, p. 7085–9. https://doi.org/10.1109/ICASSP48485.2024.10446567.
[53] Zhou E, Wang W, Ma S, Xie X, Kang L, Xu S, Deng Z, Gong Q, Nie Z, Yao L, Bu L, Wang F, Liu Z. Prediction of anxious depression using multimodal neuroimaging and machine learning. Neuroimage 2024;285:120499. https://doi.org/10.1016/J.NEUROIMAGE.2023.120499.
[54] Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021;20:154–70. https://doi.org/10.1002/WPS.20882.
[55] Meng X, Zhang J. Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features. IEEE Access 2020;8:159897–905. https://doi.org/10.1109/ACCESS.2020.3021092.
[56] Mokatren LS, Ansari R, Cetin AE, Leow AD, Ajilore OA, Klumpp H, Yarman Vural FT. EEG Classification by Factoring in Sensor Spatial Configuration. IEEE Access 2021;9:19053–65. https://doi.org/10.1109/ACCESS.2021.3054670.
[57] Di Matteo D, Fotinos K, Lokuge S, Mason G, Sternat T, Katzman MA, Rose J. Automated screening for social anxiety, generalized anxiety, and depression from objective smartphone-collected data: Cross-sectional study. J Med Internet Res 2021;23:e28918. https://doi.org/10.2196/28918.
[58] Tushar Umrani A, Harshavardhanan P. Hybrid feature-based anxiety detection in autism using hybrid optimization tuned artificial neural network. Biomed Signal Process Control 2022;76:103699. https://doi.org/10.1016/J.BSPC.2022.103699.
[59] Aldayel M, Al-Nafjan A. A comprehensive exploration of machine learning techniques for EEG-based anxiety detection. PeerJ Comput Sci 2024;10:e1829. https://doi.org/10.7717/PEERJ-CS.1829/SUPP-2.
[60] Aljabri A, Rashwan D, Qasem R, Fakeeh R, Albeladi R, Sassi N. Overcoming Speech Anxiety Using Virtual Reality with Voice and Heart Rate Analysis. Proceedings - International Conference on Developments in eSystems Engineering, DeSE, vol. 2020- December, Institute of Electrical and Electronics Engineers Inc.; 2020, p. 311–6. https://doi.org/10.1109/DESE51703.2020.9450783.
[61] Madison A, Vasey M, Emery CF, Kiecolt-Glaser JK. Social anxiety symptoms, heart rate variability, and vocal emotion recognition in women: evidence for parasympathetically-mediated positivity bias. Anxiety Stress Coping 2021;34:243–57. https://doi.org/10.1080/10615806.2020.1839733/SUPPL_FILE/GASC_A_1839733_SM9662.DOCX.
[62] Olatinwo DD, Abu-Mahfouz A, Hancke G, Myburgh H. IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients. Sensors 2023;23:2948. https://doi.org/10.3390/S23062948.
[63] Brueckner R, Kwon N, Subramanian V, Blaylock N, O’Connell H. Audio-Based Detection of Anxiety and Depression via Vocal Biomarkers. Lecture Notes in Networks and Systems, vol. 919 LNNS, Springer, Cham; 2024, p. 124–41. https://doi.org/10.1007/978-3-031-53960-2_9.
[64] Jaiswal S, Song S, Valstar M. Automatic prediction of Depression and Anxiety from behaviour and personality attributes. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019, Institute of Electrical and Electronics Engineers Inc.; 2019, p. 454–60. https://doi.org/10.1109/ACII.2019.8925456.
[65] Pampouchidou A, Pediaditis M, Kazantzaki E, Sfakianakis S, Apostolaki IA, Argyraki K, Manousos D, Meriaudeau F, Marias K, Yang F, Tsiknakis M, Basta M, Vgontzas AN, Simos P. Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation. Mach Vis Appl 2020;31:1–19. https://doi.org/10.1007/S00138-020-01080-7/METRICS.
[66] Lidle LR, Schmitz J. Assessing Visual Avoidance of Faces During Real-Life Social Stress in Children with Social Anxiety Disorder: A Mobile Eye-Tracking Study. Child Psychiatry Hum Dev 2024;55:24–35. https://doi.org/10.1007/S10578-022-01383-Y/TABLES/5.
[67] Lueken U, Straube B, Yang Y, Hahn T, Beesdo-Baum K, Wittchen HU, Konrad C, Ströhle A, Wittmann A, Gerlach AL, Pfleiderer B, Arolt V, Kircher T. Separating depressive comorbidity from panic disorder: A combined functional magnetic resonance imaging and machine learning approach. J Affect Disord 2015;184:182–92. https://doi.org/10.1016/J.JAD.2015.05.052.
[68] Makovac E, Watson DR, Meeten F, Garfinkel SN, Cercignani M, Critchley HD, Ottaviani C. Amygdala functional connectivity as a longitudinal biomarker of symptom changes in generalized anxiety. Soc Cogn Affect Neurosci 2016;11:1719–28. https://doi.org/10.1093/SCAN/NSW091.
[69] Felger JC. Imaging the Role of Inflammation in Mood and Anxiety-related Disorders. Curr Neuropharmacol 2017;16:533–58. https://doi.org/10.2174/1570159X15666171123201142.
[70] Boeke EA, Holmes AJ, Phelps EA. Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample. Biol Psychiatry Cogn Neurosci Neuroimaging 2020;5:799–807. https://doi.org/10.1016/J.BPSC.2019.05.018.
[71] Fang J, Li G, Xu W, Liu W, Chen G, Zhu Y, Luo Y, Luo X, Zhou B. Exploring Abnormal Brain Functional Connectivity in Healthy Adults, Depressive Disorder, and Generalized Anxiety Disorder through EEG Signals: A Machine Learning Approach for Triple Classification. Brain Sci 2024;14:245. https://doi.org/10.3390/BRAINSCI14030245.
[72] Shen JH, Rudzicz F. Detecting Anxiety through Reddit. Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology—From Linguistic Signal to Clinical Reality, Stroudsburg, PA, USA: Association for Computational Linguistics; 2017, p. 58–65. https://doi.org/10.18653/V1/W17-3107.
[73] Thorstad R, Wolff P. Predicting future mental illness from social media: A big-data approach. Behav Res Methods 2019;51:1586–600. https://doi.org/10.3758/S13428-019-01235-Z/TABLES/4.
[74] Owen D, Collados JC, Espinosa-Anke L. Towards Preemptive Detection of Depression and Anxiety in Twitter. ArXiv 2020:82–9.
[75] Kim J, Lee J, Park E, Han J. A deep learning model for detecting mental illness from user content on social media. Sci Rep 2020;10:1–6. https://doi.org/10.1038/s41598-020-68764-y.
[76] Alshanketi F. Revolutionizing Generalized Anxiety Disorder Detection using a Deep Learning Approach with MGADHF Architecture on Social Media. International Journal of Advanced Computer Science and Applications 2024;15:918–26. https://doi.org/10.14569/IJACSA.2024.0150192.
[77] Silber-Varod V, Levi-Belz Y, Amir N, Kreiner H, Lovett R. Do social anxiety individuals hesitate more? The prosodic profile of hesitation disfluencies in Social Anxiety Disorder individuals. Speech Prosody 2016 2016:1211–5. https://doi.org/10.21437/SpeechProsody.2016-249.
[78] Sahu NK, Yadav M, Lone HR. Unveiling Social Anxiety: Analyzing Acoustic and Linguistic Traits in Impromptu Speech within a Controlled Study. ACM Journal on Computing and Sustainable Societies 2024;2:1–19. https://doi.org/10.1145/3657245.
[79] Zheng C, Zhang T, Chen X, Zhang H, Wan J, Wu B. Assessing learners’ English public speaking anxiety with multimodal deep learning technologies. Comput Assist Lang Learn 2024:1–29. https://doi.org/10.1080/09588221.2024.2351129.
[80] Albuquerque L, Valente ARS, Teixeira A, Figueiredo D, Sa-Couto P, Oliveira C. Association between acoustic speech features and non-severe levels of anxiety and depression symptoms across lifespan. PLoS One 2021;16:e0248842. https://doi.org/10.1371/JOURNAL.PONE.0248842.
[81] Jain SH, Powers BW, Hawkins JB, Brownstein JS. The digital phenotype. Nat Biotechnol 2015;33:462–3. https://doi.org/10.1038/nbt.3223.
[82] Insel TR. Digital Phenotyping: Technology for a New Science of Behavior. JAMA 2017;318:1215–6. https://doi.org/10.1001/JAMA.2017.11295.
[83] Elvevåg B, Cohen AS, Wolters MK, Whalley HC, Gountouna VE, Kuznetsova KA, Watson AR, Nicodemus KK. An examination of the language construct in NIMH’s research domain criteria: Time for reconceptualization! American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 2016;171:904–19. https://doi.org/10.1002/AJMG.B.32438.
[84] Guntuku SC, Preotiuc-Pietro D, Eichstaedt JC, Ungar LH. What Twitter Profile and Posted Images Reveal about Depression and Anxiety. Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, Association for the Advancement of Artificial Intelligence; 2019, p. 236–46. https://doi.org/10.1609/ICWSM.V13I01.3225.
[85] Zarate D, Ball M, Prokofieva M, Kostakos V, Stavropoulos V. Identifying self-disclosed anxiety on Twitter: A natural language processing approach. Psychiatry Res 2023;330:115579. https://doi.org/10.1016/J.PSYCHRES.2023.115579.
[86] Teferra BG, Borwein S, DeSouza DD, Rose J. Screening for Generalized Anxiety Disorder From Acoustic and Linguistic Features of Impromptu Speech: Prediction Model Evaluation Study. JMIR Form Res 2022;6:e39998. https://doi.org/10.2196/39998.
[87] Kim S, Cha J, Kim D, Park E. Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts. J Med Internet Res 2023;25:e49074. https://doi.org/10.2196/49074.
[88] Faghel-Soubeyrand S, Lecomte T, Bravo MA, Lepage M, Potvin S, Abdel-Baki A, Villeneuve M, Gosselin F. Abnormal visual representations associated with confusion of perceived facial expression in schizophrenia with social anxiety disorder. NPJ Schizophr 2020;6:1–9. https://doi.org/10.1038/s41537-020-00116-1.
[89] Tseng HH, Huang YL, Chen JT, Liang KY, Lin CC, Chen SH. Facial and prosodic emotion recognition in social anxiety disorder. Cogn Neuropsychiatry 2017;22:331–45. https://doi.org/10.1080/13546805.2017.1330190.
[90] Weise T, Pérez-Toro PA, Deitermann A, Hoffmann B, Demir K can, Straetz T, Nöth E, Maier A, Kallert T, Yang SH. Multi-modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video. Workshop on Machine Learning for Multimodal Healthcare Data, vol. 14315, Springer; 2023, p. 26–42. https://doi.org/10.1007/978-3-031-47679-2_3/COVER.
[91] Mo H, Li Y, Han P, Liao X, Zhang W, Ding S. SFF-DA: Spatiotemporal Feature Fusion for Nonintrusively Detecting Anxiety. IEEE Trans Instrum Meas 2024;73:1–13. https://doi.org/10.1109/TIM.2023.3341132.
[92] Xie Y, Yang B, Lu X, Zheng M, Fan C, Bi X, Zhou S, Li Y. Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), vol. 2020, Institute of Electrical and Electronics Engineers Inc.; 2020, p. 1503–6. https://doi.org/10.1109/EMBC44109.2020.9176471.
[93] Hahn T, Kircher T, Straube B, Wittchen HU, Konrad C, Ströhle A, Wittmann A, Pfleiderer B, Reif A, Arolt V, Lueken U. Predicting Treatment Response to Cognitive Behavioral Therapy in Panic Disorder With Agoraphobia by Integrating Local Neural Information. JAMA Psychiatry 2015;72:68–74. https://doi.org/10.1001/JAMAPSYCHIATRY.2014.1741.
[94] Frick A, Engman J, Alaie I, Björkstrand J, Gingnell M, Larsson EM, Eriksson E, Wahlstedt K, Fredrikson M, Furmark T. Neuroimaging, genetic, clinical, and demographic predictors of treatment response in patients with social anxiety disorder. J Affect Disord 2020;261:230–7. https://doi.org/10.1016/J.JAD.2019.10.027.
[95] Al-Ezzi A, Kamel N, Faye I, Gunaseli E. Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder. Front Psychol 2020;11:517065. https://doi.org/10.3389/FPSYG.2020.00730/BIBTEX.
[96] Qiao J, Li A, Cao C, Wang Z, Sun J, Xu G. Aberrant functional network connectivity as a biomarker of generalized anxiety disorder. Front Hum Neurosci 2017;11:626. https://doi.org/10.3389/FNHUM.2017.00626/BIBTEX.
[97] Ko K, Jones A, Francis D, Robidoux S, McArthur G. Physiological correlates of anxiety in childhood and adolescence: A systematic review and meta-analysis. Stress and Health 2024;40:e3388. https://doi.org/10.1002/SMI.3388.
[98] Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: A systematic review. Laryngoscope Investig Otolaryngol 2020;5:96–116. https://doi.org/10.1002/LIO2.354.
[99] Marin MF, Zsido RG, Song H, Lasko NB, Killgore WDS, Rauch SL, Simon NM, Milad MR. Skin Conductance Responses and Neural Activations During Fear Conditioning and Extinction Recall Across Anxiety Disorders. JAMA Psychiatry 2017;74:622–31. https://doi.org/10.1001/JAMAPSYCHIATRY.2017.0329.
[100] Tomasi J, Zai CC, Zai G, Herbert D, Richter MA, Mohiuddin AG, Tiwari AK, Kennedy JL. Investigating the association of anxiety disorders with heart rate variability measured using a wearable device. J Affect Disord 2024;351:569–78. https://doi.org/10.1016/J.JAD.2024.01.137.
[101] Jacobson NC, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy 2022;149:104013. https://doi.org/10.1016/J.BRAT.2021.104013.
[102] Jacobson NC, Lekkas D, Huang R, Thomas N. Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17–18 years. J Affect Disord 2021;282:104–11. https://doi.org/10.1016/J.JAD.2020.12.086.
[103] Handouzi W, Maaoui C, Pruski A. Virtual reality exposure aided-diagnosis system for anxiety disorders: Long short-term memory architecture for three levels of anxiety recognition. Biomed Mater Eng 2023;34:491–502. https://doi.org/10.3233/BME-222542.
[104] Lee TR, Kim GH, Choi MT. Geriatric depression and anxiety screening via deep learning using activity tracking and sleep data. Int J Geriatr Psychiatry 2024;39:e6071. https://doi.org/10.1002/GPS.6071.
[105] Haritha H, Negi S, Menon RS, Kumar AA, Kumar CS. Automating anxiety detection using respiratory signal analysis. 2017 - IEEE International Symposium on Technologies for Smart Cities, IEEE; 2017, p. 1–5. https://doi.org/10.1109/TENCONSPRING.2017.8069995.
[106] Barua PD, Vicnesh J, Lih OS, Palmer EE, Yamakawa T, Kobayashi M, Acharya UR. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn 2022;18:1–22. https://doi.org/10.1007/S11571-022-09904-0/FIGURES/1.
[107] Bone D, Mertens J, Zane E, Lee S, Narayanan S, Grossman R. Acoustic-Prosodic and Physiological Response to Stressful Interactions in Children with Autism Spectrum Disorder. Interspeech, 2017, p. 147–51. https://doi.org/10.21437/Interspeech.2017-179.
[108] Jiang Z, Seyedi S, Griner E, Abbasi A, Rad AB, Kwon H, Cotes RO, Clifford GD. Multimodal Mental Health Digital Biomarker Analysis from Remote Interviews using Facial, Vocal, Linguistic, and Cardiovascular Patterns. IEEE J Biomed Health Inform 2024. https://doi.org/10.1109/JBHI.2024.3352075.
[109] Weeks JW, Srivastav A, Howell AN, Menatti AR. “Speaking more than words”: Classifying men with social anxiety disorder via vocal acoustic analyses of diagnostic interviews. J Psychopathol Behav Assess 2016;38:30–41. https://doi.org/10.1007/S10862-015-9495-9/METRICS.
[110] Binelli C, Muñiz A, Subira S, Navines R, Blanco-Hinojo L, Perez-Garcia D, Crippa J, Farré M, Pérez-Jurado L, Pujol J, Martin-Santos R. Facial emotion processing in patients with social anxiety disorder and Williams–Beuren syndrome: an fMRI study. Journal of Psychiatry and Neuroscience 2016;41:182–91. https://doi.org/10.1503/JPN.140384.
[111] Liu F, Guo W, Fouche JP, Wang Y, Wang W, Ding J, Zeng L, Qiu C, Gong Q, Zhang W, Chen H. Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct Funct 2015;220:101–15. https://doi.org/10.1007/S00429-013-0641-4/METRICS.
[112] Capozzi F, Manti F, Di Trani M, Romani M, Vigliante M, Sogos C. Children’s and parent’s psychological profiles in selective mutism and generalized anxiety disorder: a clinical study. Eur Child Adolesc Psychiatry 2018;27:775–83. https://doi.org/10.1007/S00787-017-1075-Y/METRICS.
[113] Mokatren LS, Ansari R, Cetin AE, Leow AD, Ajilore O, Klumpp H, Vural FTY. EEG Classification based on Image Configuration in Social Anxiety Disorder. 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), vol. 2019- March, IEEE Computer Society; 2019, p. 577–80. https://doi.org/10.1109/NER.2019.8717152.
[114] Gavrilescu M, Vizireanu N. Predicting Depression, Anxiety, and Stress Levels from Videos Using the Facial Action Coding System. Sensors 2019;19:3693. https://doi.org/10.3390/S19173693.
[115] Ochi K, Ono N, Owada K, Kojima M, Kuroda M, Sagayama S, Yamasue H. Quantification of speech and synchrony in the conversation of adults with autism spectrum disorder. PLoS One 2019;14:e0225377. https://doi.org/10.1371/JOURNAL.PONE.0225377.
[116] Elgendi M, Menon C. Assessing Anxiety Disorders Using Wearable Devices: Challenges and Future Directions. Brain Sci 2019;9:50. https://doi.org/10.3390/BRAINSCI9030050.
[117] Rammohan RA, Medikonda J, Pothiyil DI. Speech Signal-Based Modelling of Basic Emotions to Analyse Compound Emotion: Anxiety. 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc.; 2020, p. 218–23. https://doi.org/10.1109/DISCOVER50404.2020.9278094.
[118] Nirjhar EH, Behzadan AH, Chaspari T, Chaspari T 2021. Knowledge-and Data-Driven Models of Multimodal Trajectories of Public Speaking Anxiety in Real and Virtual Settings. Proceedings of the 2021 International Conference on Multimodal Interaction, New York, NY, USA: ACM; 2021, p. 712–6. https://doi.org/10.1145/3462244.
[119] Kwon N, Hossain S, Blaylock N, O’connell H, Hachen N, Gwin J. Detecting Anxiety and Depression from Phone Conversations using x-vectors. InProc. Workshop on Speech, Music and Mind, 2022, p. 1–5. https://doi.org/10.21437/SMM.2022-1.
[120] Wanderley Espinola C, Gomes JC, Mônica Silva Pereira J, dos Santos WP. Detection of major depressive disorder, bipolar disorder, schizophrenia and generalized anxiety disorder using vocal acoustic analysis and machine learning: an exploratory study. Research on Biomedical Engineering 2022;38:813–29. https://doi.org/10.1007/S42600-022-00222-2/METRICS.
[121] Choudhary S, Thomas N, Alshamrani S, Srinivasan G, Ellenberger J, Nawaz U, Cohen R. A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study. JMIR Med Inform 2022;10:e38943. https://doi.org/10.2196/38943.
[122] Zeghari R, Gindt M, König A, Nachon O, Lindsay H, Robert P, Fernandez A, Askenazy F. Study protocol: how does parental stress measured by clinical scales and voice acoustic stress markers predict children’s response to PTSD trauma-focused therapies? BMJ Open 2023;13:e068026. https://doi.org/10.1136/BMJOPEN-2022-068026.
[123] Jyothi S, Sneha S, Preetha P, Priyanka S, Taskeen R. A Deep Convolutional Neural Network Based Prediction System For Autism Spectrum Disorder In Facial Images. International Journal of Research in Engineering and Science 2023;11:319–24.
[124] Rao S, Shi H. EFFECTS OF PSYCHOLOGICAL STRESS AND ANXIETY ON PERFORMANCE AND COPING STRATEGIES IN ATHLETES. Revista Multidisciplinar de Las Ciencias Del Deporte 2024;24. https://doi.org/10.15366/RIMCAFD2024.94.030.
[125] Wang Y, Chen B, Liu H, Hu Z. Understanding Flow Experience in Video Learning by Multimodal Data. Int J Hum Comput Interact 2024;40:3144–58. https://doi.org/10.1080/10447318.2023.2181878.
[126] Laukka P, Linnman C, Åhs F, Pissiota A, Frans Ö, Faria V, Michelgård Å, Appel L, Fredrikson M, Furmark T. In a nervous voice: Acoustic analysis and perception of anxiety in social phobics’ speech. J Nonverbal Behav 2008;32:195–214. https://doi.org/10.1007/S10919-008-0055-9/METRICS.
[127] Sauter DA, Eisner F, Calder AJ, Scott SK. Perceptual Cues in Nonverbal Vocal Expressions of Emotion. Quarterly Journal of Experimental Psychology 2010;63:2251–72. https://doi.org/10.1080/17470211003721642.
[128] Billeci L, Varanini M, Tonacci A, Vaz M, Summavielle T, Sebastião R, Ribeiro RP. Multimodal Classification of Anxiety Based on Physiological Signals. Applied Science 2023;13:6368. https://doi.org/10.3390/app13116368.
[129] Baghdadi A, Aribi Y, Fourati R, Halouani N, Siarry P, Alimi A. Psychological stimulation for anxious states detection based on EEG-related features. J Ambient Intell Humaniz Comput 2021;12:8519–33. https://doi.org/10.1007/S12652-020-02586-8/METRICS.
[130] Šalkevicius J, Damaševičius R, Maskeliunas R, Laukienė I. Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals. Electronics (Basel) 2019;8:1039. https://doi.org/10.3390/ELECTRONICS8091039.
[131] Giannakakis G, Grigoriadis D, Tsiknakis M. Detection of stress/anxiety state from EEG features during video watching. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Institute of Electrical and Electronics Engineers Inc.; 2015, p. 6034–7. https://doi.org/10.1109/EMBC.2015.7319767.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Rakhi Nagpal, Saravjeet Singh, Aditi Moudgil

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.