Pilot study for non-invasive diabetes detection through classification of photoplethysmography signals using convolutional neural networks
Authors
Department of Physics and Electronics, Faculty of Science, University of Kelaniya
Rumesh Rajapaksha
Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya
Thosini Kumarika
Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya
Dinusha Perera
Department of Family Medicine, Faculty of Medicine, University of Kelaniya
Charith Jayathilaka
Department of Physics and Electronics, Faculty of Science, University of Kelaniya
Janitha A. Liyanage
Department of Chemistry, Faculty of Science, University of Kelaniya
Sudath R.D. Kalingamudali
Department of Physics and Electronics, Faculty of Science, University of Kelaniya
DOI: https://doi.org/10.4038/jmtr.v9i1.17 | Journal eISSN: 3051-5262
Language: English
Page range: 85 - 95
Published on: Oct 15, 2024
Published by: Faculty of Graduate Studies (FGS), University of Kelaniya
In partnership with: Paradigm Publishing Services
© 2024 Hiruni J. Gunathilaka, Rumesh Rajapaksha, Thosini Kumarika, Dinusha Perera, Uditha Herath, Charith Jayathilaka, Janitha A. Liyanage, Sudath R.D. Kalingamudali, published by Faculty of Graduate Studies (FGS), University of Kelaniya
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License.