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Journal Abstract Search

249 related items for PubMed ID: 37542944

  • 1. A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography.
    Pankaj, Kumar A, Komaragiri R, Kumar M.
    Comput Methods Programs Biomed; 2023 Oct; 240():107716. PubMed ID: 37542944
    [Abstract] [Full Text] [Related]

  • 2. Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework.
    Pankaj, Kumar A, Komaragiri R, Kumar M.
    Phys Eng Sci Med; 2023 Dec; 46(4):1589-1605. PubMed ID: 37747644
    [Abstract] [Full Text] [Related]

  • 3. Optimized deep neural network models for blood pressure classification using Fourier analysis-based time-frequency spectrogram of photoplethysmography signal.
    Pankaj, Kumar A, Kumar M, Komaragiri R.
    Biomed Eng Lett; 2023 Nov; 13(4):739-750. PubMed ID: 37872982
    [Abstract] [Full Text] [Related]

  • 4. Prediction of arterial blood pressure waveforms from photoplethysmogram signals via fully convolutional neural networks.
    Cheng J, Xu Y, Song R, Liu Y, Li C, Chen X.
    Comput Biol Med; 2021 Nov; 138():104877. PubMed ID: 34571436
    [Abstract] [Full Text] [Related]

  • 5. Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals.
    Rastegar S, Gholam Hosseini H, Lowe A.
    Sensors (Basel); 2023 Jan 22; 23(3):. PubMed ID: 36772300
    [Abstract] [Full Text] [Related]

  • 6. Blood Pressure Estimation Using Photoplethysmography Only: Comparison between Different Machine Learning Approaches.
    Khalid SG, Zhang J, Chen F, Zheng D.
    J Healthc Eng; 2018 Jan 22; 2018():1548647. PubMed ID: 30425819
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  • 8. Cuffless blood pressure estimation using chaotic features of photoplethysmograms and parallel convolutional neural network.
    Khodabakhshi MB, Eslamyeh N, Sadredini SZ, Ghamari M.
    Comput Methods Programs Biomed; 2022 Nov 22; 226():107131. PubMed ID: 36137326
    [Abstract] [Full Text] [Related]

  • 9. Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning.
    Wu J, Liang H, Ding C, Huang X, Huang J, Peng Q.
    Int J Hypertens; 2021 Nov 22; 2021():9938584. PubMed ID: 34394983
    [Abstract] [Full Text] [Related]

  • 10. Fully convolutional neural network and PPG signal for arterial blood pressure waveform estimation.
    Zhou Y, Tan Z, Liu Y, Cheng H.
    Physiol Meas; 2023 Sep 01; 44(7):. PubMed ID: 37402386
    [Abstract] [Full Text] [Related]

  • 11. Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.
    Fuadah YN, Lim KM.
    Diagnostics (Basel); 2022 Nov 21; 12(11):. PubMed ID: 36428946
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  • 13. Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification.
    Liang Y, Chen Z, Ward R, Elgendi M.
    Biosensors (Basel); 2018 Oct 26; 8(4):. PubMed ID: 30373211
    [Abstract] [Full Text] [Related]

  • 14. DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography.
    Kim DK, Kim YT, Kim H, Kim DJ.
    IEEE J Biomed Health Inform; 2022 Aug 26; 26(8):3697-3707. PubMed ID: 35511844
    [Abstract] [Full Text] [Related]

  • 15. Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals.
    Malayeri AB, Khodabakhshi MB.
    Sci Rep; 2022 Apr 22; 12(1):6633. PubMed ID: 35459260
    [Abstract] [Full Text] [Related]

  • 16. An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.
    Pal R, Rudas A, Kim S, Chiang JN, Barney A, Cannesson M.
    Comput Methods Programs Biomed; 2024 Sep 22; 254():108283. PubMed ID: 38901273
    [Abstract] [Full Text] [Related]

  • 17. A clinical set-up for noninvasive blood pressure monitoring using two photoplethysmograms and based on convolutional neural networks.
    Esmaelpoor J, Sanat ZM, Moradi MH.
    Biomed Tech (Berl); 2021 Aug 26; 66(4):375-385. PubMed ID: 33826809
    [Abstract] [Full Text] [Related]

  • 18. Personalized Blood Pressure Estimation Using Photoplethysmography: A Transfer Learning Approach.
    Leitner J, Chiang PH, Dey S.
    IEEE J Biomed Health Inform; 2022 Jan 26; 26(1):218-228. PubMed ID: 34077378
    [Abstract] [Full Text] [Related]

  • 19. Estimating Systolic Blood Pressure Using Convolutional Neural Networks.
    Rastegar S, Gholamhosseini H, Lowe A, Mehdipour F, Lindén M.
    Stud Health Technol Inform; 2019 Jan 26; 261():143-149. PubMed ID: 31156106
    [Abstract] [Full Text] [Related]

  • 20. Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative.
    Sarkar S, Ghosh A.
    Comput Biol Med; 2023 Nov 26; 166():107558. PubMed ID: 37806054
    [Abstract] [Full Text] [Related]

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