Feature analysis of electroencephalography in patients with depression

Risa Nakamura, Yasue Mitsukura

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

10 Citations (Scopus)


Recently many people are suffering from mental illnesses like depression worldwide. Although they are ambiguous and have difficulties in grasping the states of patients, in fact they lower their quality of life. The total loss of economics, life and quality of life by depression is big enough that it cannot be ignored. It is important for the patients to recover from depression and also for the healthy controls not to become depression. So correct diagnosis and treatment are essential for the people. In actual clinical field, incorrectness of diagnosis is now regarded as issue. To construct an objective way of evaluation on depression, we set a goal of extraction of features in depressive electroencephalography (EEG). Unlike other studies in this field, this study has mainly two points of unique. Firstly, this feature analysis is using signal from just one channel located in frontal lobe (Fp1). Secondly, the acquisition of EEG was conducted during actual clinical inquiry or under similar situation. After the experiment, EEG of both depression patients and healthy controls were compared through two-sample t-test.

Original languageEnglish
Title of host publication2018 IEEE Life Sciences Conference, LSC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781538667095
Publication statusPublished - 2018 Dec 10
Event2018 IEEE Life Sciences Conference, LSC 2018 - Montreal, Canada
Duration: 2018 Oct 282018 Oct 30

Publication series

Name2018 IEEE Life Sciences Conference, LSC 2018


Other2018 IEEE Life Sciences Conference, LSC 2018


  • Analysis
  • Clinical Settings
  • Comparison
  • Depression
  • Diagnosis
  • EEG
  • Electroencephalography
  • Feature
  • Hamd
  • Interview
  • Objective
  • Power Spectrum
  • Psychiatric
  • Single Channel
  • T-Test

ASJC Scopus subject areas

  • Signal Processing
  • Medicine (miscellaneous)
  • Health Informatics
  • Instrumentation


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