Depression screening from voice samples of patients affected by parkinson’s disease

Name: Depression screening from voice samples of patients affected by parkinson’s disease
Author: Özkanca, Yasin Serdar, Öztürk, M. G., Ekmekci, Merve Nur, Atkins, D. C., Demiroğlu, Cenk, Ghomi, R. H.

İsim	Depression screening from voice samples of patients affected by parkinson’s disease
Yazar	Özkanca, Yasin Serdar, Öztürk, M. G., Ekmekci, Merve Nur, Atkins, D. C., Demiroğlu, Cenk, Ghomi, R. H.
Basım Tarihi:	2019-05-01
Basım Yeri	- S. Karger AG
Konu	Audio features, Deep neural networks, Depression screening, Feature selection, Parkinson's disease, Voice biomarkers, Voice technology
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2504-110X
Kayıt Numarası	8e0553b0-3b30-408f-ac9d-a6a39e7d428b
Lokasyon	Electrical & Electronics Engineering
Tarih	2019-05-01
Örnek Metin	Depression is a common mental health problem leading to significant disability worldwide. It is not only common but also commonly co-occurs with other mental and neurological illnesses. Parkinson's disease (PD) gives rise to symptoms directly impairing a person's ability to function. Early diagnosis and detection of depression can aid in treatment, but diagnosis typically requires an interview with a health provider or a structured diagnostic questionnaire. Thus, unobtrusive measures to monitor depression symptoms in daily life could have great utility in screening depression for clinical treatment. Vocal biomarkers of depression are a potentially effective method of assessing depression symptoms in daily life, which is the focus of the current research. We have a database of 921 unique PD patients and their self-assessment of whether they felt depressed or not. Voice recordings from these patients were used to extract paralinguistic features, which served as inputs to machine learning and deep learning techniques to predict depression. The results are presented here, and the limitations are discussed given the nature of the recordings which lack language content. Our models achieved accuracies as high as 0.77 in classifying depressed and nondepressed subjects accurately using their voice features and PD severity. We found depression and severity of PD had a correlation coefficient of 0.3936, providing a valuable feature when predicting depression from voice. Our results indicate a clear correlation between feeling depressed and PD severity. Voice may be an effective digital biomarker to screen for depression among PD patients.
DOI	10.1159/000500354
Cilt	3

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Depression screening from voice samples of patients affected by parkinson’s disease

Yazar Özkanca, Yasin Serdar, Öztürk, M. G., Ekmekci, Merve Nur, Atkins, D. C., Demiroğlu, Cenk, Ghomi, R. H.

Basım Tarihi 2019-05-01

Basım Yeri - S. Karger AG

Konu Audio features, Deep neural networks, Depression screening, Feature selection, Parkinson's disease, Voice biomarkers, Voice technology

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2504-110X

Kayıt Numarası 8e0553b0-3b30-408f-ac9d-a6a39e7d428b

Lokasyon Electrical & Electronics Engineering

Tarih 2019-05-01

Örnek Metin Depression is a common mental health problem leading to significant disability worldwide. It is not only common but also commonly co-occurs with other mental and neurological illnesses. Parkinson's disease (PD) gives rise to symptoms directly impairing a person's ability to function. Early diagnosis and detection of depression can aid in treatment, but diagnosis typically requires an interview with a health provider or a structured diagnostic questionnaire. Thus, unobtrusive measures to monitor depression symptoms in daily life could have great utility in screening depression for clinical treatment. Vocal biomarkers of depression are a potentially effective method of assessing depression symptoms in daily life, which is the focus of the current research. We have a database of 921 unique PD patients and their self-assessment of whether they felt depressed or not. Voice recordings from these patients were used to extract paralinguistic features, which served as inputs to machine learning and deep learning techniques to predict depression. The results are presented here, and the limitations are discussed given the nature of the recordings which lack language content. Our models achieved accuracies as high as 0.77 in classifying depressed and nondepressed subjects accurately using their voice features and PD severity. We found depression and severity of PD had a correlation coefficient of 0.3936, providing a valuable feature when predicting depression from voice. Our results indicate a clear correlation between feeling depressed and PD severity. Voice may be an effective digital biomarker to screen for depression among PD patients.

DOI 10.1159/000500354

Cilt 3