Easy detection of rare dementia based on speech analysis

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

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

Abstract

The increase in the number of dementia patients is a serious problem in developed countries. It is important to diagnose an underlying disease correctly because dementia has treatments depending on the disease. We have been investigating frontotemporal lobar degeneration (FTLD), which is one of the underlying diseases. This paper presents a speech analysis-based FTLD screening system. We used speech features to classify FTLD, Alzheimer's disease (AD) and HC. We confirmed that our method can classify three groups with accuracy of 0.81 and macro F-measure of 0.74. Our screening system has the potential to detect FTLD through short speech.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages954-956
Number of pages3
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 13-10-2020
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 13-10-202016-10-2020

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
CountryJapan
CityKobe
Period13-10-2016-10-20

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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