Depressive Mood Assessment Method Based on Emotion Level Derived from Voice: Comparison of Voice Features of Individuals with Major Depressive Disorders and Healthy Controls

Shinohara, Shuji; Nakamura, Mitsuteru; Omiya, Yasuhiro; Higuchi, Masakazu; Hagiwara, Naoki; Mitsuyoshi, Shunji; Toda, Hiroyuki; Saito, Taku; Tanichi, Masaaki; Yoshino, Aihide; Tokuno, Shinichi

doi:10.3390/ijerph18105435

Cited by 15 publications

(12 citation statements)

References 31 publications

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“…To summarize, people with depression used a much larger percentage of words related to the past and sadness emotions. Similarly, Shinohara et al [ 27 ] used a “vitality” score based on emotional elements of speech (ie, joy , anger ) and showed a negative correlation between the vitality score and HAM-D scores. Arevian et al [ 22 ] analyzed the types of word used and found, among others, more negative emotion and less complex word use in patients with depression.…”

Section: Resultsmentioning

confidence: 99%

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

Ettore¹,

Müller²,

Hinze³

et al. 2023

JMIR Ment Health

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Background Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful. Objective We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common. Methods We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma. Results A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes. Conclusions Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

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Section: Resultsmentioning

confidence: 99%

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

Ettore¹,

Müller²,

Hinze³

et al. 2023

JMIR Ment Health

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“…Therefore, voice analysis has been attracting attention in recent years. The authors have developed an index called “vitality” [ 24 ], which estimates the degree of mental health from the voice and have distributed it as a software development kit and a smart phone/web application (Mind Monitoring System: MIMOSYS, PST Inc., Yokohama, Japan) [ 25 ]. It is widely available to many companies and general users [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Psychological Stress Determined by Voice Analysis and Periodontal Status: A Cohort Study

Maruyama

Ekuni

Higuchi

et al. 2022

IJERPH

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In modern society, evaluation and management of psychological stress may be important for the prevention of periodontal disease. The purpose of this study was to examine the relationship between psychological stress (vitality and mental activity) evaluated by Mind Monitoring System (MIMOSYS) and periodontal status. Forty students of Okayama University underwent the oral examination and self-reported questionnaire on the first day (baseline) and the 14th day (follow-up). Voice recording was performed every day with the MIMOSYS app during the whole study period. The participants completed the Patient Health Questionnaire (PHQ)-9 and Beck Depression Inventory (BDI) at baseline and at follow-up. Spearman’s rank correlation coefficient was used to determine the significance of correlations among variables. The PHQ-9 and BDI scores were negatively correlated with vitality in the morning. Change in vitality in the morning was significantly correlated with changes in periodontal inflammation. Mental activity was significantly correlated with change in mean probing pocket depth. This result shows that measurement of psychological stress using a voice-based tool to assess mental health may contribute to the early detection of periodontal disease.

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“…Feelings are reflected in people’s voice and facial expressions, and this common knowledge has also been scientifically substantiated [ 15 , 16 ]. Such evidence has driven a recent surge of research interest in acoustic biomarkers for predicting depression and stress levels [ 17 , 18 , 19 ]. Simplicity is a major advantage of such approaches, i.e., voice recordings can be collected non-invasively and remotely without any specialized equipment besides a microphone.…”

Section: Introductionmentioning

confidence: 99%

“…Our research group has also focused on depression’s association with emotional expression. In a previous study, we developed composite metrics for quantifying mental health—“vitality” and “mental activity”—that combine different emotional components of the voice [ 19 ]. In subsequent work, we showed evidence for this measure’s effectiveness in detecting depression and monitoring stress due to life events [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach

Higuchi

Nakamura

Shinohara

et al. 2022

IJERPH

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In general, it is common knowledge that people’s feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician’s diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

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Depressive Mood Assessment Method Based on Emotion Level Derived from Voice: Comparison of Voice Features of Individuals with Major Depressive Disorders and Healthy Controls

Cited by 15 publications

References 31 publications

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

Relationship between Psychological Stress Determined by Voice Analysis and Periodontal Status: A Cohort Study

Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach

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