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 *

120 related articles for article (PubMed ID: 35353692)

  • 21. Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection.
    Wang R; Fan J; Li Y
    IEEE J Biomed Health Inform; 2020 Sep; 24(9):2461-2472. PubMed ID: 32287022
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Using GPS, accelerometry and heart rate to predict outdoor graded walking energy expenditure.
    de Müllenheim PY; Chaudru S; Emily M; Gernigon M; Mahé G; Bickert S; Prioux J; Noury-Desvaux B; Le Faucheur A
    J Sci Med Sport; 2018 Feb; 21(2):166-172. PubMed ID: 29110991
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units.
    Popp WL; Richner L; Brogioli M; Wilms B; Spengler CM; Curt AEP; Starkey ML; Gassert R
    Front Neurol; 2018; 9():478. PubMed ID: 30018586
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation.
    Eom H; Roh J; Hariyani YS; Baek S; Lee S; Kim S; Park C
    Sensors (Basel); 2021 Oct; 21(21):. PubMed ID: 34770365
    [TBL] [Abstract][Full Text] [Related]  

  • 25. EMG, heart rate, and accelerometer as estimators of energy expenditure in locomotion.
    Tikkanen O; Kärkkäinen S; Haakana P; Kallinen M; Pullinen T; Finni T
    Med Sci Sports Exerc; 2014 Sep; 46(9):1831-9. PubMed ID: 24504428
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Depth Estimation from Light Field Geometry Using Convolutional Neural Networks.
    Han L; Huang X; Shi Z; Zheng S
    Sensors (Basel); 2021 Sep; 21(18):. PubMed ID: 34577268
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Use of a Wireless Network of Accelerometers for Improved Measurement of Human Energy Expenditure.
    Montoye AH; Dong B; Biswas S; Pfeiffer KA
    Electronics (Basel); 2014; 3(2):205-220. PubMed ID: 25530874
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram.
    Li Z; Zhou D; Wan L; Li J; Mou W
    J Electrocardiol; 2020; 58():105-112. PubMed ID: 31812617
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Automatic 12-Leading Electrocardiogram Classification Network with Deformable Convolution.
    Xie Y; Qin L; Tan H; Li X; Liu B; Wang H
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():882-885. PubMed ID: 34891431
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Estimation of the energy expenditure of grazing ruminants by incorporating dynamic body acceleration into a conventional energy requirement system.
    Miwa M; Oishi K; Anzai H; Kumagai H; Ieiri S; Hirooka H
    J Anim Sci; 2017 Feb; 95(2):901-909. PubMed ID: 28380599
    [TBL] [Abstract][Full Text] [Related]  

  • 31. LwF-ECG: Learning-without-forgetting approach for electrocardiogram heartbeat classification based on memory with task selector.
    Ammour N; Alhichri H; Bazi Y; Alajlan N
    Comput Biol Med; 2021 Oct; 137():104807. PubMed ID: 34496312
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Heart rate measurements as an index of energy expenditure and energy balance in ruminants: a review.
    Brosh A
    J Anim Sci; 2007 May; 85(5):1213-27. PubMed ID: 17224466
    [TBL] [Abstract][Full Text] [Related]  

  • 33. An original piecewise model for computing energy expenditure from accelerometer and heart rate signals.
    Romero-Ugalde HM; Garnotel M; Doron M; Jallon P; Charpentier G; Franc S; Huneker E; Simon C; Bonnet S
    Physiol Meas; 2017 Jul; 38(8):1599-1615. PubMed ID: 28665293
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Validity of the simultaneous heart rate-motion sensor technique for measuring energy expenditure.
    Strath SJ; Bassett DR; Thompson DL; Swartz AM
    Med Sci Sports Exerc; 2002 May; 34(5):888-94. PubMed ID: 11984311
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Blood glucose estimation based on ECG signal.
    Fellah Arbi K; Soulimane S; Saffih F; Bechar MA; Azzoug O
    Phys Eng Sci Med; 2023 Mar; 46(1):255-264. PubMed ID: 36595189
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Predicting energy expenditure from photo-plethysmographic measurements of heart rate under beta blocker therapy: Data driven personalization strategies based on mixed models.
    Bonomi AG; Goldenberg S; Papini G; Kraal J; Stut W; Sartor F; Kemps H
    Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():7642-6. PubMed ID: 26738062
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Accuracy of the Multisensory Wristwatch Polar Vantage's Estimation of Energy Expenditure in Various Activities: Instrument Validation Study.
    Gilgen-Ammann R; Schweizer T; Wyss T
    JMIR Mhealth Uhealth; 2019 Oct; 7(10):e14534. PubMed ID: 31579020
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Automated ECG classification using a non-local convolutional block attention module.
    Wang J; Qiao X; Liu C; Wang X; Liu Y; Yao L; Zhang H
    Comput Methods Programs Biomed; 2021 May; 203():106006. PubMed ID: 33735660
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Prediction of energy expenditure in a whole body indirect calorimeter at both low and high levels of physical activity.
    de Jonge L; Nguyen T; Smith SR; Zachwieja JJ; Roy HJ; Bray GA
    Int J Obes Relat Metab Disord; 2001 Jul; 25(7):929-34. PubMed ID: 11443488
    [TBL] [Abstract][Full Text] [Related]  

  • 40. End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising.
    Fotiadou E; Konopczyński T; Hesser J; Vullings R
    Physiol Meas; 2020 Feb; 41(1):015005. PubMed ID: 31918422
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 6.