A Dementia Classification Based on Speech Analysis of Casual Talk During a Clinical Interview

Shunya Hanai, Shohei Kato, Takuto Sakuma, Reiko Ohdake, Michihito Masuda, Hirohisa Watanabe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a dementia screening system based on spontaneous speech analysis to assist general practitioners. Our research aims to detect dementia, especially frontotemporal lobar degeneration (FTLD). We propose a new method of dementia detection by casual talk during a clinical interview that is simpler and quicker than previous studies. The method uses speech features from an answer to the question to automatically detect dementia and classify dementia types. We recruited 136 Japanese subjects (50 males and 86 females between the ages of 45 and 84) in this study. Two types of dementia patients, FTLD and Alzheimer's disease (AD), were recruited. We used features from an answer to a clinical interview to classify and confirm that our method can detect 77% of people with dementia.

Original languageEnglish
Title of host publicationLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages38-40
Number of pages3
ISBN (Electronic)9781665419048
DOIs
Publication statusPublished - 2022
Event4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 - Osaka, Japan
Duration: 07-03-202209-03-2022

Publication series

NameLifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies

Conference

Conference4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022
Country/TerritoryJapan
CityOsaka
Period07-03-2209-03-22

All Science Journal Classification (ASJC) codes

  • Agricultural and Biological Sciences (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Instrumentation
  • Education

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