Detecting postpartum depression in depressed people by speech features

Jingying Wang; Xiaoyun Sui; Bin Hu; Jonathan Flint; Shuotian Bai; Yuanbo Gao; Yang Zhou; Tingshao Zhu

doi:10.1007/978-3-319-74521-3_46

Detecting postpartum depression in depressed people by speech features

Jingying Wang, Xiaoyun Sui, Bin Hu, Jonathan Flint, Shuotian Bai, Yuanbo Gao, Yang Zhou, Tingshao Zhu^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects.

Original language	English
Title of host publication	Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers
Editors	Bo Hu, Qiaohong Zu
Publisher	Springer Verlag
Pages	433-442
Number of pages	10
ISBN (Print)	9783319745206
DOIs	https://doi.org/10.1007/978-3-319-74521-3_46
Publication status	Published - 2018
Externally published	Yes
Event	3rd International Conference on Human Centered Computing, HCC 2017 - Kazan, Russian Federation Duration: 7 Aug 2017 → 9 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10745 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Human Centered Computing, HCC 2017
Country/Territory	Russian Federation
City	Kazan
Period	7/08/17 → 9/08/17

Keywords

Classification
Depression
Detecting
Postpartum depression
Speech features

Access to Document

10.1007/978-3-319-74521-3_46

Cite this

Wang, J., Sui, X., Hu, B., Flint, J., Bai, S., Gao, Y., Zhou, Y., & Zhu, T. (2018). Detecting postpartum depression in depressed people by speech features. In B. Hu, & Q. Zu (Eds.), Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers (pp. 433-442). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10745 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-74521-3_46

Wang, Jingying ; Sui, Xiaoyun ; Hu, Bin et al. / Detecting postpartum depression in depressed people by speech features. Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers. editor / Bo Hu ; Qiaohong Zu. Springer Verlag, 2018. pp. 433-442 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e7b871e6d58c46f0b56b6c6120460855,

title = "Detecting postpartum depression in depressed people by speech features",

abstract = "Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects.",

keywords = "Classification, Depression, Detecting, Postpartum depression, Speech features",

author = "Jingying Wang and Xiaoyun Sui and Bin Hu and Jonathan Flint and Shuotian Bai and Yuanbo Gao and Yang Zhou and Tingshao Zhu",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 3rd International Conference on Human Centered Computing, HCC 2017 ; Conference date: 07-08-2017 Through 09-08-2017",

year = "2018",

doi = "10.1007/978-3-319-74521-3_46",

language = "English",

isbn = "9783319745206",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Wang, J, Sui, X, Hu, B, Flint, J, Bai, S, Gao, Y, Zhou, Y & Zhu, T 2018, Detecting postpartum depression in depressed people by speech features. in B Hu & Q Zu (eds), Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10745 LNCS, Springer Verlag, pp. 433-442, 3rd International Conference on Human Centered Computing, HCC 2017, Kazan, Russian Federation, 7/08/17. https://doi.org/10.1007/978-3-319-74521-3_46

Detecting postpartum depression in depressed people by speech features. / Wang, Jingying; Sui, Xiaoyun; Hu, Bin et al.
Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers. ed. / Bo Hu; Qiaohong Zu. Springer Verlag, 2018. p. 433-442 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10745 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting postpartum depression in depressed people by speech features

AU - Wang, Jingying

AU - Sui, Xiaoyun

AU - Hu, Bin

AU - Flint, Jonathan

AU - Bai, Shuotian

AU - Gao, Yuanbo

AU - Zhou, Yang

AU - Zhu, Tingshao

N1 - Publisher Copyright: © Springer International Publishing AG 2018.

PY - 2018

Y1 - 2018

N2 - Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects.

AB - Postpartum depression (PPD) is a depressive disorder with peripartum onset, which brings heavy burden to individuals and their families. In this paper, we propose to detect PPD in depressed people via voices. We used openSMILE for feature extraction, selected Sequential Floating Forward Selection (SFFS) algorithm for feature selection, tried different settings of features, set 5-fold cross validation and applied Support Vector Machine (SVM) on Weka for training and testing different models. The best predictive performance among our models is 69%, which suggests that the speech features could be used as a potential behavioral indicator for identifying PPD in depression. We also found that a combined impact of features and content of questions contribute to the prediction. After dimension reduction, the average value of F-measure was increased 5.2%, and the precision of PPD was rose to 75%. Comparing with demographic questions, the features of emotional induction questions have better predictive effects.

KW - Classification

KW - Depression

KW - Detecting

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KW - Speech features

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DO - 10.1007/978-3-319-74521-3_46

M3 - Conference contribution

AN - SCOPUS:85042103855

SN - 9783319745206

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers

A2 - Hu, Bo

A2 - Zu, Qiaohong

PB - Springer Verlag

T2 - 3rd International Conference on Human Centered Computing, HCC 2017

Y2 - 7 August 2017 through 9 August 2017

ER -

Wang J, Sui X, Hu B, Flint J, Bai S, Gao Y et al. Detecting postpartum depression in depressed people by speech features. In Hu B, Zu Q, editors, Human Centered Computing - 3rd International Conference, HCC 2017, Revised Selected Papers. Springer Verlag. 2018. p. 433-442. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-74521-3_46

Detecting postpartum depression in depressed people by speech features

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