BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

153 related articles for article (PubMed ID: 26490145)

  • 1. Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks.
    Li X; Hu B; Shen J; Xu T; Retcliffe M
    J Med Syst; 2015 Dec; 39(12):187. PubMed ID: 26490145
    [TBL] [Abstract][Full Text] [Related]  

  • 2. EEG-based mild depressive detection using feature selection methods and classifiers.
    Li X; Hu B; Sun S; Cai H
    Comput Methods Programs Biomed; 2016 Nov; 136():151-61. PubMed ID: 27686712
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.
    Hosseinifard B; Moradi MH; Rostami R
    Comput Methods Programs Biomed; 2013 Mar; 109(3):339-45. PubMed ID: 23122719
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Study on Feature Selection Methods for Depression Detection Using Three-Electrode EEG Data.
    Cai H; Chen Y; Han J; Zhang X; Hu B
    Interdiscip Sci; 2018 Sep; 10(3):558-565. PubMed ID: 29728983
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification.
    Siuly S; Li Y
    Comput Methods Programs Biomed; 2015 Apr; 119(1):29-42. PubMed ID: 25704869
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Human emotion classification based on multiple physiological signals by wearable system.
    Liu X; Wang Q; Liu D; Wang Y; Zhang Y; Bai O; Sun J
    Technol Health Care; 2018; 26(S1):459-469. PubMed ID: 29758969
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Classification of Perceived Mental Stress Using A Commercially Available EEG Headband.
    Arsalan A; Majid M; Butt AR; Anwar SM
    IEEE J Biomed Health Inform; 2019 Nov; 23(6):2257-2264. PubMed ID: 31283515
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Methods for classifying depression in single channel EEG using linear and nonlinear signal analysis.
    Bachmann M; Päeske L; Kalev K; Aarma K; Lehtmets A; Ööpik P; Lass J; Hinrikus H
    Comput Methods Programs Biomed; 2018 Mar; 155():11-17. PubMed ID: 29512491
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.
    Torabi A; Daliri MR; Sabzposhan SH
    Australas Phys Eng Sci Med; 2017 Dec; 40(4):785-797. PubMed ID: 28887746
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals.
    Acharya UR; Sudarshan VK; Adeli H; Santhosh J; Koh JE; Puthankatti SD; Adeli A
    Eur Neurol; 2015; 74(1-2):79-83. PubMed ID: 26303033
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).
    Mumtaz W; Ali SSA; Yasin MAM; Malik AS
    Med Biol Eng Comput; 2018 Feb; 56(2):233-246. PubMed ID: 28702811
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Depression diagnosis using machine intelligence based on spatiospectrotemporal analysis of multi-channel EEG.
    Nassibi A; Papavassiliou C; Atashzar SF
    Med Biol Eng Comput; 2022 Nov; 60(11):3187-3202. PubMed ID: 36115006
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data.
    Zhu J; Wang Z; Gong T; Zeng S; Li X; Hu B; Li J; Sun S; Zhang L
    IEEE Trans Nanobioscience; 2020 Jul; 19(3):527-537. PubMed ID: 32340958
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automatic detection and classification of artifacts in single-channel EEG.
    Olund T; Duun-Henriksen J; Kjaer TW; Sorensen HB
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():922-5. PubMed ID: 25570110
    [TBL] [Abstract][Full Text] [Related]  

  • 15. EEG-based mild depression recognition using convolutional neural network.
    Li X; La R; Wang Y; Niu J; Zeng S; Sun S; Zhu J
    Med Biol Eng Comput; 2019 Jun; 57(6):1341-1352. PubMed ID: 30778842
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization.
    Tian F; Zhu L; Shi Q; Wang R; Zhang L; Dong Q; Qian K; Zhao Q; Hu B
    IEEE Trans Biomed Circuits Syst; 2023 Dec; 17(6):1305-1318. PubMed ID: 37402182
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Classification of Depression Patients and Normal Subjects Based on Electroencephalogram (EEG) Signal Using Alpha Power and Theta Asymmetry.
    Mahato S; Paul S
    J Med Syst; 2019 Dec; 44(1):28. PubMed ID: 31834531
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Depression recognition using machine learning methods with different feature generation strategies.
    Li X; Zhang X; Zhu J; Mao W; Sun S; Wang Z; Xia C; Hu B
    Artif Intell Med; 2019 Aug; 99():101696. PubMed ID: 31606115
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Classification of EEG signals using neural network and logistic regression.
    Subasi A; Erçelebi E
    Comput Methods Programs Biomed; 2005 May; 78(2):87-99. PubMed ID: 15848265
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Detection of Depression and Scaling of Severity Using Six Channel EEG Data.
    Mahato S; Goyal N; Ram D; Paul S
    J Med Syst; 2020 May; 44(7):118. PubMed ID: 32435986
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.