TY - GEN
T1 - Study on indicators for depression in the elderly using voice and attribute information
AU - Higuchi, Masakazu
AU - Shinohara, Shuji
AU - Nakamura, Mitsuteru
AU - Omiya, Yasuhiro
AU - Hagiwara, Naoki
AU - Takano, Takeshi
AU - Mitsuyoshi, Shunji
AU - Tokuno, Shinichi
N1 - Funding Information:
Acknowledgements. This research is (partially) supported by the Center of Innovation Program from Japan Science and Technology Agency, JST. This work was supported by JSPS KAKENHI Grant Numbers JP15H03002 and JP17K01404.
Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - As the age of the human population increases worldwide, depression in elderly patients has become a problem in medical care. In this study, we analyzed voice-emotion component data, attribute data, and Beck Depression Inventory (BDI) scores by multivariate analysis, particularly in the elderly, and proposed evaluation indicators for estimating the state of depression of elderly patients. We divided the data into two groups according to BDI scores: a state of depression and the absence of this state. The labels distinguishing the two groups were dependent variables, while the voice-emotion component and attribute information were set as independent variables, and we performed logistic regression analysis on the data. We obtained a prediction model with significantly sufficient fitness. In the receiver operating characteristic curve for the proposed depression evaluation indicator, a sorting performance with an area under the curve of approximately 0.93 was obtained.
AB - As the age of the human population increases worldwide, depression in elderly patients has become a problem in medical care. In this study, we analyzed voice-emotion component data, attribute data, and Beck Depression Inventory (BDI) scores by multivariate analysis, particularly in the elderly, and proposed evaluation indicators for estimating the state of depression of elderly patients. We divided the data into two groups according to BDI scores: a state of depression and the absence of this state. The labels distinguishing the two groups were dependent variables, while the voice-emotion component and attribute information were set as independent variables, and we performed logistic regression analysis on the data. We obtained a prediction model with significantly sufficient fitness. In the receiver operating characteristic curve for the proposed depression evaluation indicator, a sorting performance with an area under the curve of approximately 0.93 was obtained.
KW - Attribute information
KW - Beck depression inventory
KW - Depression
KW - Emotion recognition
KW - Voice
UR - http://www.scopus.com/inward/record.url?scp=85049044868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049044868&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93644-4_7
DO - 10.1007/978-3-319-93644-4_7
M3 - Conference contribution
AN - SCOPUS:85049044868
SN - 9783319936437
T3 - Communications in Computer and Information Science
SP - 127
EP - 146
BT - Information and Communication Technologies for Ageing Well and e-Health - 3rd International Conference, ICT4AWE 2017, Revised Selected Papers
A2 - Maciaszek, Leszek
A2 - O Donoghue, John
A2 - Molloy, William
A2 - Rocker, Carsten
A2 - Ziefle, Martina
PB - Springer Verlag
T2 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2017
Y2 - 28 April 2017 through 29 April 2017
ER -