Speech-based dementia classification for FTLD diagnosis support

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

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

Abstract

This paper proposes a screening system to automatically detect a Frontotemporal Lobar Degeneration (FTLD) and support a diagnosis of a general practitioner. Dementia results from a variety of diseases that primarily or secondarily affect the brain. It is important to diagnose an underlying disease correctly. We have been investigating FTLD, which is one of diseases. We took into account the specific symptoms, used speech features to classify FTLD, Alzheimer’s disease (AD) and healthy control (HC). We confirmed that our method can classify three groups with accuracy of 0.84 and macro F-measure of 0.79. We also showed the effectiveness of linguistic features in FTLD detection.

Original languageEnglish
Title of host publicationLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages344-346
Number of pages3
ISBN (Electronic)9781665418751
DOIs
Publication statusPublished - 09-03-2021
Event3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021 - Nara, Japan
Duration: 09-03-202111-03-2021

Publication series

NameLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies

Conference

Conference3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021
Country/TerritoryJapan
CityNara
Period09-03-2111-03-21

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health(social science)
  • Biochemistry
  • Artificial Intelligence
  • Computer Science Applications

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