BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

118 related articles for article (PubMed ID: 36909628)

  • 1. NOVEL APPROACH EXPLAINS SPATIO-SPECTRAL INTERACTIONS IN RAW ELECTROENCEPHALOGRAM DEEP LEARNING CLASSIFIERS.
    Ellis CA; Sattiraju A; Miller RL; Calhoun VD
    bioRxiv; 2023 Feb; ():. PubMed ID: 36909628
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data.
    Ellis CA; Sattiraju A; Miller RL; Calhoun VD
    bioRxiv; 2023 Oct; ():. PubMed ID: 37873255
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.
    Ellis CA; Sancho ML; Miller RL; Calhoun VD
    bioRxiv; 2024 Mar; ():. PubMed ID: 38562835
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder by Pretraining Deep Learning Models with Single Channel Sleep Stage Data.
    Ellis CA; Sattiraju A; Miller RL; Calhoun VD
    bioRxiv; 2023 May; ():. PubMed ID: 37398050
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data.
    Ellis CA; Miller RL; Calhoun VD
    Front Neuroinform; 2022; 16():872035. PubMed ID: 35712676
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach.
    Sancho ML; Ellis CA; Miller RL; Calhoun VD
    bioRxiv; 2024 Feb; ():. PubMed ID: 38405889
    [TBL] [Abstract][Full Text] [Related]  

  • 7. An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis.
    Sattiraju A; Ellis CA; Miller RL; Calhoun VD
    bioRxiv; 2023 Oct; ():. PubMed ID: 37398173
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Novel methods for elucidating modality importance in multimodal electrophysiology classifiers.
    Ellis CA; Sendi MSE; Zhang R; Carbajal DA; Wang MD; Miller RL; Calhoun VD
    Front Neuroinform; 2023; 17():1123376. PubMed ID: 37006636
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs.
    Ellis CA; Miller RL; Calhoun VD
    Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():1643-1646. PubMed ID: 36086296
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Toward practical machine-learning-based diagnosis for drug-naïve women with major depressive disorder using EEG channel reduction approach.
    Shim M; Hwang HJ; Lee SH
    J Affect Disord; 2023 Oct; 338():199-206. PubMed ID: 37302509
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram Data.
    Ellis CA; Miller RL; Calhoun VD
    bioRxiv; 2023 Dec; ():. PubMed ID: 38187601
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population.
    Kaushik P; Yang H; Roy PP; van Vugt M
    Sci Rep; 2023 May; 13(1):7467. PubMed ID: 37156879
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME.
    Hussain I; Jany R; Boyer R; Azad A; Alyami SA; Park SJ; Hasan MM; Hossain MA
    Sensors (Basel); 2023 Aug; 23(17):. PubMed ID: 37687908
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis
    Ellis CA; Miller RL; Calhoun VD
    Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083554
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals.
    Khare SK; Acharya UR
    Comput Biol Med; 2023 Mar; 155():106676. PubMed ID: 36827785
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder.
    Ebrahimzadeh E; Fayaz F; Rajabion L; Seraji M; Aflaki F; Hammoud A; Taghizadeh Z; Asgarinejad M; Soltanian-Zadeh H
    Front Syst Neurosci; 2023; 17():919977. PubMed ID: 36968455
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.
    Tosun M
    Phys Eng Sci Med; 2021 Sep; 44(3):693-702. PubMed ID: 34043150
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States. A Data Driven Study.
    Jan D; de Vega M; López-Pigüi J; Padrón I
    Brain Sci; 2022 Nov; 12(11):. PubMed ID: 36358432
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features.
    Truong D; Milham M; Makeig S; Delorme A
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():1039-1042. PubMed ID: 34891466
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification.
    Al-Kaysi AM; Al-Ani A; Loo CK; Powell TY; Martin DM; Breakspear M; Boonstra TW
    J Affect Disord; 2017 Jan; 208():597-603. PubMed ID: 28029427
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.