Classification of Bipolar Disorder, Major Depressive Disorder, and Healthy State Using Voice

Higuchi, Masakazu; Tokuno, Shinichi; Nakamura, Mitsuteru; Shinohara, Shuji; Mitsuyoshi, Shunji; Omiya, Yasuhiro; Hagiwara, Naoki; Takano, Takeshi; Toda, Hiroyuki; Saito, Taku; Terashi, Hiroo; Mitoma, Hiroshi

doi:10.22159/ajpcr.2018.v11s3.30042

Cited by 11 publications

(13 citation statements)

References 12 publications

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“…The copyright holder for this preprint this version posted June 24, 2020. As mentioned earlier, except for the work of Higuchi et al (2018), we achieved better results than other previously published studies. Our work provided high classification accuracy both for depressed and healthy individuals.…”

Section: Discussionsupporting

confidence: 51%

Detection of Major Depressive Disorder Using Vocal Acoustic Analysis and Machine Learning

Espinola

Gomes

Pereira

et al. 2020

Preprint

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Purpose Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgement. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous and spiritless speech, low voice. Methods Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD, and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to to a vocal feature extraction algorithm, and to different machine learning classification techniques. Results The results showed that support vector machines (SVM) models provided the greatest classification performances for different kernels, with PUK kernel providing accuracy of 89.14% for the detection of MDD. Conclusion The use of machine learning classifiers with vocal acoustics features has shown to be very promising for the detection of major depressive disorder, but further tests with a larger sample will be necessary to validate our findings.

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Section: Discussionsupporting

confidence: 51%

Detection of Major Depressive Disorder Using Vocal Acoustic Analysis and Machine Learning

Espinola

Gomes

Pereira

et al. 2020

Preprint

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“…The mean age of the control group was 30.1 years (± 12.6 years), whereas the mean age of the depression group was 42.9 years (± 13.0 years). There is no standardization on age controlling in studies: some selected age-matched controls to their samples (Alghowinem et al 2013b;Alghowinem et al 2012;Cummins et al 2015); and some did not (Afshan et al 2018;Cannizzaro et al 2004;Higuchi et al 2018;Jiang et al 2017;Joshi et al 2013;Liu et al 2015;Ozdas et al, 2004;Scherer et al 2013). Given this heterogeneity, in this work, we assume the perspective of the majority of revised studies in which age between groups was not controlled.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, diagnosis and therapeutic decision are extremely sensitive to memory and subjectivity biases (Jiang et al 2018). Considering this, over the last decades, there has been an intense search for biomarkers for diagnosis and follow-up of psychiatric patients (Iwabuchi et al, 2013;Mundt et al 2012), most of those being expensive and invasive (Higuchi et al 2018). Despite all efforts, instruments for assessment of mental disorders still remain a conundrum (Mundt et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

et al. 2020

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Purpose Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgment. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous, and spiritless speech and low voice. Methods Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to a vocal feature extraction algorithm, and to different machine learning classification techniques. Results The results showed that random tree models with 100 trees provided the greatest classification performances. It achieved mean accuracy of 87.5575% ± 1.9490, mean kappa index, sensitivity, and specificity of 0.7508 ± 0.0319, 0.9149 ± 0.0204, and 0.8354 ± 0.0254, respectively, for the detection of MDD. Conclusion The use of machine learning classifiers with vocal acoustic features appears to be very promising for the detection of major depressive disorder in this exploratory study, but further experiments with a larger sample will be necessary to validate our findings.

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“…In this context, there was an intense search for biomarkers for diagnosis and follow-up of psychiatric patients in the last decade (Iwabuchi et al 2013;Mundt et al 2012). However, most of them are expensive and invasive (Higuchi et al 2018). Therefore, despite all efforts, objective measures for assessment of mental disorders are still unknown (Mundt et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Vocal acoustic analysis and machine learning for the identification of schizophrenia

et al. 2020

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Purpose Psychiatry still needs objective biomarkers. In the context of schizophrenia, there are speech abnormalities such as tangentiality, derailment, alogia, neologisms, poverty of speech, and aprosodia. There is a growing interest in speech signals features as possible indicators of schizophrenia. This article aims to develop an intelligent tool for detection of schizophrenia using vocal patterns and machine learning techniques. The main advantages of this type of solution are the low cost, high performance, and for being non-invasive. Methods Thirty-one individuals over 18 years old were selected, 20 with previous diagnosis of schizophrenia, and 11 healthy controls. Their speech was audio-recorded in naturalistic settings, during a routine medical assessment for psychiatric patients. In the case of healthy patients, the recordings were made in different environments. Recordings were pre-processed, excluding non-participant voices. We extracted 33 features. We used the particle swarm optimization algorithm for feature selection. Results The classifiers' performance was analyzed with four metrics: accuracy, sensibility, specificity, and kappa index. Best results were achieved when considering all 33 extracted features. Within machine models, support vector machines (SVM) models provided the greatest classification performance, with mean accuracy of 91.76% for PUK kernel. Our results outperform those from most studies published so far for the detection of schizophrenia based on acoustic patterns. Conclusion The use of machine learning classifiers using vocal parameters, in particular SVM, has shown to be very promising for the detection of schizophrenia. Nevertheless, further experiments with a larger sample will be necessary to validate our findings.

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Classification of Bipolar Disorder, Major Depressive Disorder, and Healthy State Using Voice

Cited by 11 publications

References 12 publications

Detection of Major Depressive Disorder Using Vocal Acoustic Analysis and Machine Learning

Detection of Major Depressive Disorder Using Vocal Acoustic Analysis and Machine Learning

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

Vocal acoustic analysis and machine learning for the identification of schizophrenia

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