These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

162 related articles for article (PubMed ID: 26157639)

  • 1. Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.
    Akwei-Sekyere S
    PeerJ; 2015; 3():e1086. PubMed ID: 26157639
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ECG signal denoising via empirical wavelet transform.
    Singh O; Sunkaria RK
    Australas Phys Eng Sci Med; 2017 Mar; 40(1):219-229. PubMed ID: 28035635
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Adaptive digital notch filter design on the unit circle for the removal of powerline noise from biomedical signals.
    Ferdjallah M; Barr RE
    IEEE Trans Biomed Eng; 1994 Jun; 41(6):529-36. PubMed ID: 7927372
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering.
    Singh O; Sunkaria RK
    J Med Eng Technol; 2015 Jan; 39(1):60-8. PubMed ID: 25412942
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A strong anti-noise segmentation algorithm based on variational mode decomposition and multi-wavelet for wearable heart sound acquisition system.
    Xiahou S; Liang Y; Ma M; Du M
    Rev Sci Instrum; 2022 May; 93(5):054102. PubMed ID: 35649757
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A novel application of the S-transform in removing powerline interference from biomedical signals.
    Huang CC; Liang SF; Young MS; Shaw FZ
    Physiol Meas; 2009 Jan; 30(1):13-27. PubMed ID: 19039164
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Frequency-domain digital filtering techniques for the removal of powerline noise with application to the electrocardiogram.
    Ferdjallah M; Barr RE
    Comput Biomed Res; 1990 Oct; 23(5):473-89. PubMed ID: 2225791
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Fractal and EMD based removal of baseline wander and powerline interference from ECG signals.
    Agrawal S; Gupta A
    Comput Biol Med; 2013 Nov; 43(11):1889-99. PubMed ID: 24209934
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Reference noise method of removing powerline noise from recorded signals.
    Jiruska P; Cmejla R; Powell AD; Chang WC; Vreugdenhil M; Jefferys JG
    J Neurosci Methods; 2009 Oct; 184(1):110-4. PubMed ID: 19595705
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis.
    Mijović B; De Vos M; Gligorijević I; Taelman J; Van Huffel S
    IEEE Trans Biomed Eng; 2010 Sep; 57(9):2188-96. PubMed ID: 20542760
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improving liquid chromatography-tandem mass spectrometry determinations by modifying noise frequency spectrum between two consecutive wavelet-based low-pass filtering procedures.
    Chen HP; Liao HJ; Huang CM; Wang SC; Yu SN
    J Chromatogr A; 2010 Apr; 1217(17):2804-11. PubMed ID: 20227706
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Denoising preterm EEG by signal decomposition and adaptive filtering: a comparative study.
    Navarro X; Porée F; Beuchée A; Carrault G
    Med Eng Phys; 2015 Mar; 37(3):315-20. PubMed ID: 25659233
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings.
    Mateo J; Rieta JJ
    J Med Eng Technol; 2012 Feb; 36(2):90-101. PubMed ID: 22268996
    [TBL] [Abstract][Full Text] [Related]  

  • 14. ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold.
    Xu Y; Luo M; Li T; Song G
    Sensors (Basel); 2017 Nov; 17(12):. PubMed ID: 29182591
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram.
    Mello RG; Oliveira LF; Nadal J
    Comput Methods Programs Biomed; 2007 Jul; 87(1):28-35. PubMed ID: 17548125
    [TBL] [Abstract][Full Text] [Related]  

  • 16. MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition.
    Ren X; Hu X; Wang Z; Yan Z
    Med Biol Eng Comput; 2006 May; 44(5):371-82. PubMed ID: 16937179
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Removal of artifacts in knee joint vibroarthrographic signals using ensemble empirical mode decomposition and detrended fluctuation analysis.
    Wu Y; Yang S; Zheng F; Cai S; Lu M; Wu M
    Physiol Meas; 2014 Mar; 35(3):429-39. PubMed ID: 24521557
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals.
    Tang G; Luo G; Zhang W; Yang C; Wang H
    Sensors (Basel); 2016 Jun; 16(6):. PubMed ID: 27322268
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Ideal filtering approach on DCT domain for biomedical signals: index blocked DCT filtering method (IB-DCTFM).
    Shin HS; Lee C; Lee M
    J Med Syst; 2010 Aug; 34(4):741-53. PubMed ID: 20703930
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Application of Empirical Mode Decomposition Combined With Notch Filtering for Interpretation of Surface Electromyograms During Functional Electrical Stimulation.
    Pilkar R; Yarossi M; Ramanujam A; Rajagopalan V; Bayram MB; Mitchell M; Canton S; Forrest G
    IEEE Trans Neural Syst Rehabil Eng; 2017 Aug; 25(8):1268-1277. PubMed ID: 27834646
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.