Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques.
- DOI
- 10.3390/bios15070408
- Published
- 2025 Jun 24
- Container
- Biosensors
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/bios15070408,
title = {Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques.},
author = {Salamea-Palacios C and Montalvo-López M and Orellana-Peralta R and Viñanzaca-Figueroa J},
year = {2025},
journal = {Biosensors},
doi = {10.3390/bios15070408},
url = {https://doi.org/10.3390/bios15070408}
}RIS
TY - JOUR TI - Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques. AU - Salamea-Palacios C AU - Montalvo-López M AU - Orellana-Peralta R AU - Viñanzaca-Figueroa J PY - 2025 JO - Biosensors DO - 10.3390/bios15070408 UR - https://doi.org/10.3390/bios15070408 ER -
APA
C, S., M, M., R, O., & J, V. (2025). Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques.. Biosensors. https://doi.org/10.3390/bios15070408
Source records
- pubmed · retrieved 2026-09-25T07:14:00.193Z