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 *

137 related articles for article (PubMed ID: 34677211)

  • 1. Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes.
    D'Addio G; Donisi L; Cesarelli G; Amitrano F; Coccia A; La Rovere MT; Ricciardi C
    Bioengineering (Basel); 2021 Oct; 8(10):. PubMed ID: 34677211
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Time-domain heart rate variability features for automatic congestive heart failure prediction.
    Moses JC; Adibi S; Angelova M; Islam SMS
    ESC Heart Fail; 2024 Feb; 11(1):378-389. PubMed ID: 38009405
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis.
    Selek MB; Yesilkaya B; Egeli SS; Isler Y
    Proc Inst Mech Eng H; 2021 Dec; 235(12):1479-1488. PubMed ID: 34365841
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning based congestive heart failure detection using feature importance ranking of multimodal features.
    Hussain L; Aziz W; Khan IR; Alkinani MH; Alowibdi JS
    Math Biosci Eng; 2020 Nov; 18(1):69-91. PubMed ID: 33525081
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A machine learning approach to classifying New York Heart Association (NYHA) heart failure.
    Jandy K; Weichbroth P
    Sci Rep; 2024 May; 14(1):11496. PubMed ID: 38769444
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Loss of lag-response curvilinearity of indices of heart rate variability in congestive heart failure.
    Thakre TP; Smith ML
    BMC Cardiovasc Disord; 2006 Jun; 6():27. PubMed ID: 16768800
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability.
    Shahbazi F; Asl BM
    Comput Methods Programs Biomed; 2015 Nov; 122(2):191-8. PubMed ID: 26344584
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Application of the Poincaré plot to heart rate variability: a new measure of functional status in heart failure.
    Kamen PW; Tonkin AM
    Aust N Z J Med; 1995 Feb; 25(1):18-26. PubMed ID: 7786239
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Complex correlation measure: a novel descriptor for Poincaré plot.
    Karmakar CK; Khandoker AH; Gubbi J; Palaniswami M
    Biomed Eng Online; 2009 Aug; 8():17. PubMed ID: 19674482
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Preliminary Results on Density Poincare Plot Based Atrial Fibrillation Detection from Premature Atrial/Ventricular Contractions
    Bashar SK; Han D; Zieneddin F; Ding E; Walkey AJ; McManus DD; Chon KH
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():2594-2597. PubMed ID: 33018537
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.
    Sudarshan VK; Acharya UR; Oh SL; Adam M; Tan JH; Chua CK; Chua KP; Tan RS
    Comput Biol Med; 2017 Apr; 83():48-58. PubMed ID: 28231511
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Is there an association between the nutritional and functional parameters and congestive heart failure severity?
    Duarte RRP; Gonzalez MC; Oliveira JF; Goulart MR; Castro I
    Clin Nutr; 2021 May; 40(5):3354-3359. PubMed ID: 33229242
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Congestive heart failure detection using random forest classifier.
    Masetic Z; Subasi A
    Comput Methods Programs Biomed; 2016 Jul; 130():54-64. PubMed ID: 27208521
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement.
    Chen W; Zheng L; Li K; Wang Q; Liu G; Jiang Q
    PLoS One; 2016; 11(11):e0165304. PubMed ID: 27835634
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset.
    Donisi L; Cesarelli G; Castaldo A; De Lucia DR; Nessuno F; Spadarella G; Ricciardi C
    J Imaging; 2021 Oct; 7(10):. PubMed ID: 34677301
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.
    Li F; Zhao C; Xia Z; Wang Y; Zhou X; Li GZ
    BMC Complement Altern Med; 2012 Aug; 12():127. PubMed ID: 22898352
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Gaussian process-based kernel as a diagnostic model for prediction of type 2 diabetes mellitus risk using non-linear heart rate variability features.
    Shashikant R; Chaskar U; Phadke L; Patil C
    Biomed Eng Lett; 2021 Aug; 11(3):273-286. PubMed ID: 34350053
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots.
    Goshvarpour A; Abbasi A; Goshvarpour A
    Australas Phys Eng Sci Med; 2017 Sep; 40(3):617-629. PubMed ID: 28717902
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Poincaré plot analysis of cerebral blood flow signals: Feature extraction and classification methods for apnea detection.
    González C; Jensen EW; Gambús PL; Vallverdú M
    PLoS One; 2018; 13(12):e0208642. PubMed ID: 30532232
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Classification tree for risk assessment in patients suffering from congestive heart failure via long-term heart rate variability.
    Melillo P; De Luca N; Bracale M; Pecchia L
    IEEE J Biomed Health Inform; 2013 May; 17(3):727-33. PubMed ID: 24592473
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.