BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

174 related articles for article (PubMed ID: 38536782)

  • 1. A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.
    Khan S; Umar Saeed SM; Frnda J; Arsalan A; Amin R; Gantassi R; Noorani SH
    PLoS One; 2024; 19(3):e0299127. PubMed ID: 38536782
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.
    Liao SC; Wu CT; Huang HC; Cheng WT; Liu YH
    Sensors (Basel); 2017 Jun; 17(6):. PubMed ID: 28613237
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis.
    Movahed RA; Jahromi GP; Shahyad S; Meftahi GH
    J Neurosci Methods; 2021 Jul; 358():109209. PubMed ID: 33957158
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal.
    Hasanzadeh F; Mohebbi M; Rostami R
    J Affect Disord; 2019 Sep; 256():132-142. PubMed ID: 31176185
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects.
    Varone G; Gasparini S; Ferlazzo E; Ascoli M; Tripodi GG; Zucco C; Calabrese B; Cannataro M; Aguglia U
    Sensors (Basel); 2020 Feb; 20(4):. PubMed ID: 32102437
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Revealing False Positive Features in Epileptic EEG Identification.
    Lian J; Shi Y; Zhang Y; Jia W; Fan X; Zheng Y
    Int J Neural Syst; 2020 Nov; 30(11):2050017. PubMed ID: 32448016
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals.
    Durongbhan P; Zhao Y; Chen L; Zis P; De Marco M; Unwin ZC; Venneri A; He X; Li S; Zhao Y; Blackburn DJ; Sarrigiannis PG
    IEEE Trans Neural Syst Rehabil Eng; 2019 May; 27(5):826-835. PubMed ID: 30951473
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals.
    Anuragi A; Sisodia DS; Pachori RB
    Comput Biol Med; 2021 Sep; 136():104708. PubMed ID: 34358996
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Detection of k-complexes in EEG signals using a multi-domain feature extraction coupled with a least square support vector machine classifier.
    Al-Salman W; Li Y; Wen P
    Neurosci Res; 2021 Nov; 172():26-40. PubMed ID: 33965451
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.
    Zarei R; He J; Siuly S; Zhang Y
    Comput Methods Programs Biomed; 2017 Jul; 146():47-57. PubMed ID: 28688489
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Drowsiness Detection Using Ocular Indices from EEG Signal.
    Tarafder S; Badruddin N; Yahya N; Nasution AH
    Sensors (Basel); 2022 Jun; 22(13):. PubMed ID: 35808261
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning-based prediction of heat pain sensitivity by using resting-state EEG.
    Hsiao FJ; Chen WT; Pan LH; Liu HY; Wang YF; Chen SP; Lai KL; Wang SJ
    Front Biosci (Landmark Ed); 2021 Dec; 26(12):1537-1547. PubMed ID: 34994168
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine learning-based classification using electroencephalographic multi-paradigms between drug-naïve patients with depression and healthy controls.
    Jang KI; Kim S; Chae JH; Lee C
    J Affect Disord; 2023 Oct; 338():270-277. PubMed ID: 37271294
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Non-linear processing and reinforcement learning to predict rTMS treatment response in depression.
    Ebrahimzadeh E; Dehghani A; Asgarinejad M; Soltanian-Zadeh H
    Psychiatry Res Neuroimaging; 2024 Jan; 337():111764. PubMed ID: 38043370
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals.
    Siuly S; Alcin OF; Kabir E; Sengur A; Wang H; Zhang Y; Whittaker F
    IEEE Trans Neural Syst Rehabil Eng; 2020 Sep; 28(9):1966-1976. PubMed ID: 32746328
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.
    Wu CT; Huang HC; Huang S; Chen IM; Liao SC; Chen CK; Lin C; Lee SH; Chen MH; Tsai CF; Weng CH; Ko LW; Jung TP; Liu YH
    Biosensors (Basel); 2021 Dec; 11(12):. PubMed ID: 34940256
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Human state anxiety classification framework using EEG signals in response to exposure therapy.
    Muhammad F; Al-Ahmadi S
    PLoS One; 2022; 17(3):e0265679. PubMed ID: 35303027
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.