[en] [en] BACKGROUND: Voice analysis has emerged as a potential biomarker for mood state detection and monitoring in bipolar disorder (BD). The systematic review aimed to summarize the evidence for voice analysis applications in BD, examining (1) the predictive validity of voice quality outcomes for mood state detection, and (2) the correlation between voice parameters and clinical symptom scales.
METHODS: A PubMed, Scopus, and Cochrane Library search was carried out by two investigators for publications investigating voice quality in BD according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements. Studies were assessed using the modified methodological index for non-randomized studies (MINORS).
RESULTS: Of the 400 identified publications, 16 studies met the inclusion accounting for 575 BD patients. Machine learning approaches were implemented in 87.5% of studies, with classification accuracies ranging from 70.9% to 96.9%. Manic state detection showed the strongest predictive validity [area under the curve (AUC) up to 0.89], while depression detection demonstrated moderate performance (AUC: 0.66-0.78). Individual-specific models outperformed population-level approaches (correlation coefficients: 0.78 versus 0.44). Voice quality showed significant correlations with standardized clinical scales, particularly Young Mania Rating Scale and Hamilton Depression Rating Scale (normalized root mean square errors: 1.985 and 3.945, respectively). Prosodic features were examined in 81.25% of studies, with pitch consistently elevated during manic episodes. MINORS varied from 10 to 14, with notable limitations in sample size calculations and blinding procedures.
CONCLUSIONS: Voice quality is a promising biomarker in BD, particularly for manic state detection and individualized monitoring. While controlled settings showed strong performance, naturalistic applications yielded more modest results. Future research should focus on standardizing protocols across different environments and conducting large-scale longitudinal studies with robust methodological controls.
Disciplines :
Psychiatry
Author, co-author :
BRIGANTI, Giovanni ; Université de Mons - UMONS > Faculté de Médecine et de Pharmacie > Service de Médecine computationnelle et Neuropsychiatrie
Lechien, Jérôme R; Department of Surgery, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium, Division of Laryngology and Bronchoesophagology, Department of Otolaryngology Head Neck Surgery, EpiCURA Hospital, Baudour, Belgium, Department of Otolaryngology-Head and Neck Surgery, Foch Hospital, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France, Department of Otolaryngology, Elsan Hospital, Paris, France. Electronic address: Jerome.Lechien@umons.ac.be
Language :
English
Title :
Voice Quality as Digital Biomarker in Bipolar Disorder: A Systematic Review.
Carvalho, A.F., Firth, J., Vieta, E., Bipolar disorder. N Engl J Med 383 (2020), 58–66, 10.1056/NEJMra1906193.
Dias, V.V., Balanzá-Martinez, V., Soeiro-de-Souza, M.G., et al. Pharmacological approaches in bipolar disorders and the impact on cognition: a critical overview. Acta Psychiatr Scand 126 (2012), 315–331, 10.1111/j.1600-0447.2012.01910.x.
Hui Poon, S., Sim, K., J. Baldessarini, R., Pharmacological approaches for treatment-resistant bipolar disorder. Curr Neuropharmacol 13 (2015), 592–604, 10.2174/1570159×13666150630171954.
Valenza, G., Nardelli, M., Lanata, A., et al. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J Biomed Health Inform 18 (2013), 1625–1635.
Grünerbl, A., Muaremi, A., Osmani, V., et al. Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inform 19 (2014), 140–148.
Baldassano, C.F., Assessment tools for screening and monitoring bipolar disorder. Bipolar Disord 7:s1 (2005), 8–15, 10.1111/j.1399-5618.2005.00189.x.
Vanello, N., Guidi, A., Gentili, C., et al. Speech analysis for mood state characterization in bipolar patients. Annu Int 2012 (2012), 2104–2107, 10.1109/EMBC.2012.6346375.
Mouchabac, S., Conejero, I., Lakhlifi, C., et al. Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient's and practitioner's individual cognitive biases. Dialogues Clin Neurosci 23 (2021), 52–61, 10.1080/19585969.2022.2042165.
Maatoug, R., Oudin, A., Adrien, V., et al. Digital phenotype of mood disorders: a conceptual and critical review. Front Psychiatry, 13, 2022, 895860.
Kaczmarek-Majer K, Dominiak M, Antosik AZ, et al. Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: a prospective study. Acta Psychiatr Scand. n/a(n/a). doi:10.1111/acps.13735.
Larsen, E., Murton, O., Song, X., et al. Validating the efficacy and value proposition of mental fitness vocal biomarkers in a psychiatric population: prospective cohort study. Front Psychiatry, 15, 2024, 1342835, 10.3389/fpsyt.2024.1342835.
Luo, J., Wu, Y., Liu, M., et al. Differentiation between depression and bipolar disorder in child and adolescents by voice features. Child Adolesc Psychiatry Ment Health, 18, 2024, 19, 10.1186/s13034-024-00708-0.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372. Available at: https://www.bmj.com/content/372/bmj.n71.short. Accessed November 10, 2024.
Samson D, Schoelles KM. Developing the topic and structuring systematic reviews of medical tests: utility of PICOTS, analytic frameworks, decision trees, and other frameworks. Methods Guide Med Test Rev Internet. Published online 2012. Available at: https:// www.ncbi.nlm.nih.gov/sites/books/NBK98235/. Accessed November 10, 2024.
First, M.B., Yousif, L.H., Clarke, D.E., et al. DSM-5-TR: overview of what's new and what's changed. World Psychiatry, 21, 2022, 218.
Hamilton, M., A rating scale for depression. J Neurol Neurosurg Psychiatry 23 (1960), 56–62.
Young, R.C., Biggs, J.T., Ziegler, V.E., Meyer, D.A., A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry 133 (1978), 429–435.
Slim, K., Nini, E., Forestier, D., et al. Methodological index for non-randomized studies (MINORS): development and validation of a new instrument. ANZ J Surg 73 (2003), 712–716, 10.1046/j.1445-2197.2003.02748.x.
Pan, W., Deng, F., Wang, X., et al. Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls. Front Psychiatry, 14, 2023, 1079448, 10.3389/fpsyt.2023.1079448.
Cansel, N., Faruk Alcin, Ö., Furkan Yılmaz, Ö., et al. A new artificial intelligence-based clinical decision support system for diagnosis of major psychiatric diseases based on voice analysis. Psychiatr Danub 35 (2023), 489–499, 10.24869/psyd.2023.489.
Weiner, L., Guidi, A., Doignon-Camus, N., et al. Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder. Transl Psychiatry, 11, 2021, 415, 10.1038/s41398-021-01535-z.
Faurholt-Jepsen, M., Busk, J., Frost, M., et al. Voice analysis as an objective state marker in bipolar disorder. Transl Psychiatry, 6, 2016, e856, 10.1038/tp.2016.123.
Birnbaum, M.L., Abrami, A., Heisig, S., et al. Acoustic and facial features from clinical interviews for machine learning-based psychiatric diagnosis: algorithm development. JMIR Ment Health, 9, 2022, e24699, 10.2196/24699.
Faurholt-Jepsen, M., Rohani, D.A., Busk, J., et al. Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states. Int J Bipolar Disord, 9, 2021, 38, 10.1186/s40345-021-00243-3.
Faurholt-Jepsen, M., Rohani, D.A., Busk, J., et al. Discriminating between patients with unipolar disorder, bipolar disorder, and healthy control individuals based on voice features collected from naturalistic smartphone calls. Acta Psychiatr Scand 145 (2022), 255–267, 10.1111/acps.13391.
Farrús, M., Codina-Filbà, J., Escudero, J., Acoustic and prosodic information for home monitoring of bipolar disorder. Health Inform J, 27, 2021, 1460458220972755, 10.1177/1460458220972755.
Place, S., Blanch-Hartigan, D., Rubin, C., et al. Behavioral indicators on a mobile sensing platform predict clinically validated psychiatric symptoms of mood and anxiety disorders. J Med Internet Res, 19, 2017, e75, 10.2196/jmir.6678.
Arevian, A.C., Bone, D., Malandrakis, N., et al. Clinical state tracking in serious mental illness through computational analysis of speech. PloS One, 15, 2020, e0225695, 10.1371/journal.pone.0225695.
Zhang, J., Pan, Z., Gui, C., et al. Analysis on speech signal features of manic patients. J Psychiatr Res 98 (2018), 59–63, 10.1016/j.jpsychires.2017.12.012.
Guidi, A., Schoentgen, J., Bertschy, G., et al. Voice quality in patients suffering from bipolar disease. Annu Int 2015 (2015), 6106–6109, 10.1109/EMBC.2015.7319785.
