Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning

Shin, Daun; Cho, Won Ik; Park, C. Hyung Keun; Rhee, Sang Jin; Kim, Min Ji; Lee, Hyunju; Kim, Nam Soo; Ahn, Yong Min

doi:10.3390/jcm10143046

Cited by 36 publications

(25 citation statements)

References 55 publications

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“…Shin et al ( 37 ) used machine learning on data extracted from semistructured interview recordings. Previous research showed that depressed patients had simpler, lifeless voices with lower volume.…”

Section: Resultsmentioning

confidence: 99%

Digital phenotype of mood disorders: A conceptual and critical review

et al. 2022

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BackgroundMood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care.ObjectiveThe main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders.MethodsWe conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.ResultsOut of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).ConclusionThe various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.

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Section: Resultsmentioning

confidence: 99%

Digital phenotype of mood disorders: A conceptual and critical review

et al. 2022

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“…Voice recordings are closer to real life, allowing for automated quantitative analysis of voices in the future. The interview also allowed the voice to be recorded for a sufficient period (>15 minutes), unlike most previous studies that used audio recordings ranging from a few seconds to several minutes in length [23][24][25], and permitted the inclusion of colloquial and paralinguistic expressions that are informative for speech analysis [15,17]. In a recent study using the Emofilm database, which is a collection of short clips of movies in several languages, Costantini et al [18] explored and concluded that a cross-language classifier maintains a Thigh performance despite variations in languages and cultures.…”

Section: Strengthsmentioning

confidence: 99%

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality

Min¹,

Shin²,

Rhee³

et al. 2023

J Med Internet Res

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Background Assessing a patient’s suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. Objective This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. Methods We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. Results A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. Conclusions Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

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“…By overcoming subjectivity, various attempts have been made to detect and address depression and suicidal ideation. Studies have been conducted to identify depression based on the fact that people with depression exhibit greater hesitation in their voice and a monotonous tone ( 13 ). Studies are also underway that aim to predict depression and suicide risk by clustering texts published on social media ( 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker

et al. 2022

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BackgroundDepression and suicide are critical social problems worldwide, but tools to objectively diagnose them are lacking. Therefore, this study aimed to diagnose depression through machine learning and determine whether it is possible to identify groups at high risk of suicide through words spoken by the participants in a semi-structured interview.MethodsA total of 83 healthy and 83 depressed patients were recruited. All participants were recorded during the Mini-International Neuropsychiatric Interview. Through the suicide risk assessment from the interview items, participants with depression were classified into high-suicide-risk (31 participants) and low-suicide-risk (52 participants) groups. The recording was transcribed into text after only the words uttered by the participant were extracted. In addition, all participants were evaluated for depression, anxiety, suicidal ideation, and impulsivity. The chi-square test and student’s T-test were used to compare clinical variables, and the Naive Bayes classifier was used for the machine learning text model.ResultsA total of 21,376 words were extracted from all participants and the model for diagnosing patients with depression based on this text confirmed an area under the curve (AUC) of 0.905, a sensitivity of 0.699, and a specificity of 0.964. In the model that distinguished the two groups using statistically significant demographic variables, the AUC was only 0.761. The DeLong test result (p-value 0.001) confirmed that the text-based classification was superior to the demographic model. When predicting the high-suicide-risk group, the demographics-based AUC was 0.499, while the text-based one was 0.632. However, the AUC of the ensemble model incorporating demographic variables was 0.800.ConclusionThe possibility of diagnosing depression using interview text was confirmed; regarding suicide risk, the diagnosis accuracy increased when demographic variables were incorporated. Therefore, participants’ words during an interview show significant potential as an objective and diagnostic marker through machine learning.

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Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning

Cited by 36 publications

References 55 publications

Digital phenotype of mood disorders: A conceptual and critical review

Digital phenotype of mood disorders: A conceptual and critical review

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality

Detection of Depression and Suicide Risk Based on Text From Clinical Interviews Using Machine Learning: Possibility of a New Objective Diagnostic Marker

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