Discrimination of Bipolar Disorders Using Voice

Several methods have been developed for screening mentally impaired patients using biomarkers, but these methods are invasive and costly. Self-administered tests are also used as screening methods. They are non-invasive and relatively simple, but they cannot eliminate the influence of reporting bias. On the other hand, the authors have conducted studies on technologies for inferring the mental state of persons from their voices. Analysis using voice has the advantage of being noninvasive and easy to perform. This study proposes a vocal index that will distinguish between a healthy person and a bipolar I or II patient using a polytomous logistic regression analysis with patients with bipolar disorder as subjects. When the subjects were classified using the prediction model obtained from the analysis, the subjects were categorized into three groups with an accuracy of approximately \(67\%\). This result suggested that the vocal index could be a new evaluation index for discriminating between subjects with and those without bipolar disorder.

[1]  J. Bardram,et al.  Voice analysis as an objective state marker in bipolar disorder , 2016, Translational psychiatry.

[2]  R. McIntyre,et al.  Differentiating bipolar disorders from major depressive disorders: treatment implications. , 2007, Annals of clinical psychiatry : official journal of the American Academy of Clinical Psychiatrists.

[3]  Janet B W Williams,et al.  Diagnostic and Statistical Manual of Mental Disorders , 2013 .

[4]  Shinichi Tokuno,et al.  Decreased Plasma Brain-Derived Neurotrophic Factor and Vascular Endothelial Growth Factor Concentrations during Military Training , 2014, PloS one.

[5]  Oscar Mayora-Ibarra,et al.  Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients , 2016, Pervasive Mob. Comput..

[6]  Shuhei Izawa,et al.  Salivary dehydroepiandrosterone secretion in response to acute psychosocial stress and its correlations with biological and psychological changes , 2008, Biological Psychology.

[7]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[8]  Björn Schuller,et al.  Opensmile: the munich versatile and fast open-source audio feature extractor , 2010, ACM Multimedia.

[9]  D. Sheehan,et al.  The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. , 1998, The Journal of clinical psychiatry.

[10]  Masakazu Higuchi,et al.  CLASSIFICATION OF BIPOLAR DISORDER, MAJOR DEPRESSIVE DISORDER, AND HEALTHY STATE USING VOICE , 2018, Asian Journal of Pharmaceutical and Clinical Research.

[11]  J. Mundt,et al.  Vocal Acoustic Biomarkers of Depression Severity and Treatment Response , 2012, Biological Psychiatry.

[12]  R. C. Young,et al.  A Rating Scale for Mania: Reliability, Validity and Sensitivity , 1978, British Journal of Psychiatry.

[13]  Roland Göcke,et al.  An Investigation of Depressed Speech Detection: Features and Normalization , 2011, INTERSPEECH.

[14]  C. Bowden,et al.  Strategies to reduce misdiagnosis of bipolar depression. , 2001, Psychiatric services.

[15]  Zhenyu Liu,et al.  Investigation of different speech types and emotions for detecting depression using different classifiers , 2017, Speech Commun..

[16]  M. Hamilton.  A RATING SCALE FOR DEPRESSION , 1960, Journal of neurology, neurosurgery, and psychiatry.

[17]  A. Beck,et al.  An inventory for measuring depression. , 1961, Archives of general psychiatry.

[18]  Riccardo Barbieri,et al.  Instantaneous bispectral analysis of heartbeat dynamics for the assessment of major depression , 2015, 2015 Computing in Cardiology Conference (CinC).