scholarly journals 0972 Practical Use Of A Single Channel Sleep EEG For Diagnosis Of Major Depressive Disorder - Multicenter Exploratory Prospective Study (SEEDs) -

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A360-A361
Author(s):  
K Kuriyama ◽  
M Suzuki ◽  
H Kadotani ◽  
A Yoshimura ◽  
H Yoshinaka ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Prospective Study ◽  
Single Channel ◽  
Sleep Eeg ◽  
Major Depressive

Morning and evening TSH response to TRH and sleep EEG disturbances in major depressive disorder

2001 ◽  
Vol 25 (3) ◽  
pp. 534-547 ◽  
Author(s):  
Luc Staner ◽  
Fabrice Duval ◽  
Francoise Calvi-Gries ◽  
Marie-Claude Mokrani ◽  
Paul Bailey ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Sleep Eeg ◽  
Major Depressive

Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

2019 ◽  
Vol 18 (05) ◽  
pp. 1579-1603 ◽  
Author(s):  
Zhijiang Wan ◽  
Hao Zhang ◽  
Jiajin Huang ◽  
Haiyan Zhou ◽  
Jie Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Single Channel ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Method ◽  
Learning Method ◽  
Eeg Analysis ◽  
Major Depressive

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

DEPRESSION AND SMOKING: A 5-YEAR PROSPECTIVE STUDY OF PATIENTS WITH MAJOR DEPRESSIVE DISORDER

2013 ◽  
Vol 30 (6) ◽  
pp. 580-588 ◽  
Author(s):  
Irina A. K. Holma ◽  
K. Mikael Holma ◽  
Tarja K. Melartin ◽  
Mikko Ketokivi ◽  
Erkki T. Isometsä
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Prospective Study ◽  
Major Depressive

A 5-year prospective study of predictors for disability pension among patients with major depressive disorder

2011 ◽  
Vol 125 (4) ◽  
pp. 325-334 ◽  
Author(s):  
I. A. K. Holma ◽  
K. M. Holma ◽  
T. K. Melartin ◽  
H. J. Rytsälä ◽  
E. T. Isometsä
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Prospective Study ◽  
Disability Pension ◽  
Major Depressive
Sign in / Sign up

Export Citation Format

Share Document