Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques

Photoplethysmography Feature Extraction for Non-Invasive Glucose Estimation by Means of MFCC and Machine Learning Techniques

Diabetes Mellitus is considered one of the most widespread diseases in the world. Traditional glucose monitoring devices carry discomfort and risks associated with the frequent extraction of blood from users. The present article proposes a noninvasive glucose estimation system based on the applicati...

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Bibliographic Details
Main Authors: Christian Salamea-Palacios, Melissa Montalvo-López, Raquel Orellana-Peralta, Javier Viñanzaca-Figueroa
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Biosensors
Subjects:
biomedical systems
medical applications
signal analysis
processing techniques
neural networks
regression algorithm systems
Online Access:https://www.mdpi.com/2079-6374/15/7/408
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