Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model

Huang, Kun-Yi; Wu, Chung-Hsien; Su, Ming-Hsiang; Kuo, Yu‐Ting

doi:10.1109/taffc.2018.2803178

Cited by 33 publications

(16 citation statements)

References 43 publications

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“…Comorbidity is one reason why the National Institute of Mental Health has developed the Research Domain Criteria with the goal of deconstructing diagnoses with biomarkers—from genetic to behavioral—to predict and improve response to treatments . Therefore, algorithms trained on behavioral descriptors could provide likelihood estimates for different disorders to aid clinicians in differential diagnosis (eg, determining whether a patient meets criteria for unipolar depression or bipolar disorder), help detect risk for chronic psychiatric disorders, psychiatric episodes, or suicidal behavior; and over time learn to predict the best treatment given multimodal (genetic, brain‐imaging, behavioral) data . Therefore, complementing clinical interviews with machine learning models trained on the recordings of these interviews could improve outcomes, save clinicians' time, reduce health care costs, and make treatment planning more efficient.…”

Section: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

250

268

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Objective There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings. Results 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder. Conclusion Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability. Level of Evidence 3a

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Section: Introductionmentioning

confidence: 99%

Automated assessment of psychiatric disorders using speech: A systematic review

Low

Bentley

Ghosh

2020

Laryngoscope Investig Oto

250

268

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“…Most recently, bipolar disorder is classified into two significant forms i.e. depressive and unipolar disorder and investigation is carried out to identify it Huang et al [12]. This study analyzes the speech response of the subject where spectral clustering is further applied to achieve non-biased classification.…”

Section: Related Workmentioning

confidence: 99%

Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder

Kumar¹,

Saxena²

2022

IJECE

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In the present state of health and wellness, mental illness is always deemed less importance compared to other forms of physical illness. In reality, mental illness causes serious multi-dimensional adverse effect to the subject with respect to personal life, social life, as well as financial stability. In the area of mental illness, bipolar disorder is one of the most prominent type which can be triggered by any external stimulation to the subject suffering from this illness. There diagnosis as well as treatment process of bipolar disorder is very much different from other form of illness where the first step of impediment is the correct diagnosis itself. According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. Which is characterized by specific mood associated with depression and mania. However, there is no study associated with mixed-mood episode detection which is characterized by combination of various symptoms of bipolar disorder in random, unpredictable, and uncertain manner. Hence, the model contributes to obtain granular information with dynamics of mood transition. The simulated outcome of the proposed system in MATLAB shows that resulting model is capable enough for detection of mixed mood episode precisely

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“…Comorbidity is one reason why the National Institute of Mental Health has developed the Research Domain Criteria with the goal of deconstructing diagnoses with biomarkers -from genetic to behavioral-to predict and improve response to treatments 30 . Therefore, algorithms trained on behavioral descriptors could provide likelihood estimates for different disorders to aid clinicians in differential diagnosis (e.g., determining whether a patient meets criteria for unipolar depression or bipolar disorder 31 ), help detect risk for chronic psychiatric disorders 32 , psychiatric episodes 33 or suicidal behavior 29 ; and over time learn to predict the best treatment given multimodal (genetic, brain-imaging, behavioral) data 34 . Therefore, complementing clinical interviews with machine learning models trained on the recordings of these interviews could improve outcomes, save clinicians' time, reduce healthcare costs, and make treatment planning more efficient.…”

Section: 119mentioning

confidence: 99%

Automated Assessment of Psychiatric Disorders Using Speech: A Systematic Review

Low¹,

Bentley²,

Ghosh³

2019

Preprint

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Objective: There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders.Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM–5). For each disorder we describe sample size, clinical evaluation method, speech-eliciting tasks, machine learning methodology, performance, and other relevant findings. Results: 1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post-traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null-hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder.Conclusion: Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of datasets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability.

show abstract

Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model

Cited by 33 publications

References 43 publications

Automated assessment of psychiatric disorders using speech: A systematic review

Automated assessment of psychiatric disorders using speech: A systematic review

Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder

Automated Assessment of Psychiatric Disorders Using Speech: A Systematic Review

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