@inproceedings{35229437eb634a7d8f328e0543588b19,
title = "Feature analysis of electroencephalography in patients with depression",
abstract = "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.",
keywords = "Analysis, Clinical Settings, Comparison, Depression, Diagnosis, EEG, Electroencephalography, Feature, Hamd, Interview, Objective, Power Spectrum, Psychiatric, Single Channel, T-Test",
author = "Risa Nakamura and Yasue Mitsukura",
note = "Funding Information: ACKNOWLEDGMENT This research was supported by MEXT KAKENHI (Scientific Research (S)) Grant Number YYK7S01. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Life Sciences Conference, LSC 2018 ; Conference date: 28-10-2018 Through 30-10-2018",
year = "2018",
month = dec,
day = "10",
doi = "10.1109/LSC.2018.8572043",
language = "English",
series = "2018 IEEE Life Sciences Conference, LSC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "53--56",
booktitle = "2018 IEEE Life Sciences Conference, LSC 2018",
}